arXiv subjects

Yu Gao

Publications and source records attributed to Yu Gao.

At least 19 recordsLinked to original sources

arXiv2026

VDGS: Visibility-Driven Large-Scale 3D Gaussian Splatting for Aerial Scene Reconstruction

Large-scale scene reconstruction is a critical foundational technology in robotic autonomous systems such as 3D mapping and autonomous driving. In recent years, 3D Gaussian Splatting (3DGS) has demonstrated remarkable advantages in both visual quality and computational efficiency, making it a promising representation for large-scale scene reconstruction. However, it still faces challenges in large-scale scenes, including excessive memory consumption and uneven viewpoint coverage caused by UAV acquisition, limiting its real-world applications. To address this, we propose VDGS, a novel 3DGS framework that incorporates camera distribution into scene modeling. VDGS introduces visibility-driven statistics for scene anchors to quantify supervision strength. These statistics are further leveraged for scene partitioning and for gradient compensation in under-optimized regions, thereby promoting balanced optimization across different regions. Extensive experiments on multiple large-scale aerial scene datasets demonstrate that, under imbalanced viewpoint distributions, VDGS consistently outperforms existing methods, while maintaining competitive performance in scenarios with more uniform view distributions.

cs.CV↗

arXiv2026

Magnetoelectric Phase Transition and Axion Dynamics

Magnetoelectric phase transitions have been experimentally studied, but no macroscopic theory has been proposed to explain their dynamical origin. In this work, we assume that the axion quasiparticle with frequency undergoes a condensation like process. We show that these magnetoelectric phase transitions can be described within a Ginzbur Landau framework by introducing a coupled dynamic parameter, the axion angle, which is proportional to the magnetoelectric coeffcient. We derive relations between the static axion angle, the axion frequency, and the phase transition temperature for different magnetoelectric materials, respectively, and compare these calculations with existing experimental results. We also connect the artificially designed Dzyaloshinskii Moriya interaction with the axion condensate like process, so that the relation between the static axion angle and the experimentally measured frequency shift can be obtained.

cond-mat.other↗

arXiv2026

Existence, uniqueness and long-time behavior of the $λ$-dissipative solutions to the two-component Hunter-Saxton system

In this paper, we construct the explicit characteristics for the $λ$-dissipative solutions ($λ\in[0,1]$) to the two-component Hunter--Saxton (2HS) system. Using these characteristics, we provide a comprehensive study of the existence, uniqueness, and asymptotic behavior of these solutions. For the fully dissipative case ($λ=1$), uniqueness follows from the absence of outgoing cusps. In the partially dissipative regime ($0<λ<1$), outgoing cusps are present. We formulate an exact Eulerian dissipation rule which identifies the energy that has already passed through wave breaking by the intrinsic condition $ρ=0$ and $u_x\geq 2/t$. This rule determines the dissipated part of the energy measure and yields uniqueness. This uniqueness applies to the classical Hunter-Saxton equation and seems to be the first uniqueness result for the general $λ$-dissipative solutions. Concerning the large-time dynamics, we show that the density $ρ$ and the singular part of the energy measure decay to zero as $t\to\infty$, indicating that all energy is eventually concentrated in the $u$-component. Moreover, we derive the leading-order asymptotic term, which takes the form of a kink-wave determined by the system's remaining energy. This kink-wave can be explicitly computed from the initial data and the dissipation parameter $λ$.

math.AP↗

arXiv2026

NeoTriFuse: Reliability-Aware Multimodal Fusion under Missingness Heterogeneity for Neonatal Mortality Risk Prediction

Neonatal mortality risk prediction from bedside monitoring data remains challenging due to extreme class imbalance, heterogeneous clinical risk factors, multi-scale temporal dynamics, and substantial missingness. We propose NeoTriFuse, a reliability-aware multimodal fusion framework for missingness-heterogeneous neonatal monitoring data. Unlike conventional multimodal approaches that treat missingness primarily as a preprocessing issue, NeoTriFuse models missingness as an explicit reliability signal that dynamically modulates modality contributions during fusion. The framework integrates static perinatal variables, local-global temporal encoders, and patient-level statistical summaries through reliability-guided gating mechanisms, while jointly optimizing mortality prediction and an auxiliary length-of-stay objective. NeoTriFuse achieves competitive performance, with an F1 score of 0.6736 +/- 0.0216 and an AUROC of 0.9454 +/- 0.0056. Ablation studies indicate that the local-global temporal architecture and patient-level summary branch contribute most substantially to predictive performance, while reliability-aware gating provides additional improvements on threshold-dependent metrics under heterogeneous observation completeness. Sensitivity analyses further suggest stable performance across nearby hyperparameter settings. Overall, the findings support reliability-aware multimodal fusion as a practical approach for neonatal mortality prediction under realistic clinical missingness conditions.

cs.LG↗

arXiv2026

Sensitivity of the Hongmeng 21cm experiment to scattering dark matter

Scattering between dark matter and baryons can cool the intergalactic medium temperature and deepen the 21cm signal. Such interactions have been proposed to explain the unusually deep 21cm absorption signal reported by EDGES in 2018. We explore the potential to detect dark matter - baryon scattering with the Hongmeng project, an upcoming Moon-orbiting satellite experiment dedicated to measuring the global 21cm signal between redshifts $11-46$. We self-consistently forward-model the simulated sky-temperature data, jointly varying the astrophysical and foreground models. We show that even with a very conservative observational strategy in which the experiment only takes data when the Earth and the Sun are both shielded by the Moon, Hongmeng can tighten the current constraints on the cross section of dark matter - baryon scattering $σ_0$ by a factor of 39 after the full mission, which lasts for five years. The prospective upper limit on $σ_0$ can reach $5.4 \times 10^{-43} {\rm cm^2}$ for dark matter masses between 0.1 MeV and 0.3 GeV. Even after only one month of operation, an improvement by a factor of 4 relative to current $σ_0$ limits can be expected.

astro-ph.CO↗

arXiv2026

Axion Dark Matter Modulated Spin Wave Interferometry

We propose a novel asymmetric spin wave interferometer to detect ultralight axion dark matter. The axion modulates spin-wave properties via a weak effective magnetic field in ferromagnets. The interferometer splits a spin-wave source into two paths of different lengths and sets them to interfere destructively. The system then converts the axion-induced phase shift into a measurable magnetization oscillation that can radiate electromagnetic waves and generate electrical signals via Faraday induction. The signal-to-noise ratios have been evaluated for three detection schemes: the linear amplifier, the single-photon detector, and the electrical signal detection approach, accounting for both magnetization fluctuation and thermal noise. The accessible axion mass range is approximately $10^{-8}$ eV to $10^{-6}$ eV, set by the spin wave propagation length and the relaxation time.

hep-ph↗

arXiv2026

Searching for Ultralight Dark Matter with M{ö}ssbauer Resonance

We investigate the feasibility of probing the interactions between ultralight scalar dark matter and atomic nuclei using a stationary Mössbauer spectroscopy scheme. The exceptional energy resolution of the Mössbauer resonance enables searches for tiny nuclear energy shifts induced by the local dark matter field. The dark matter mass range considered in this work is $10^{-18}$--$10^{-8}~\mathrm{eV}$. We present projected constraints for two candidate Mössbauer isotopes, $^{109}\mathrm{Ag}$ and $^{45}\mathrm{Sc}$, with $^{109}\mathrm{Ag}$ providing the strongest sensitivity. For $^{109}\mathrm{Ag}$, projected sensitivities as low as approximately $10^{-19}$, $10^{-22}$, and $10^{-21}~\mathrm{GeV^{-1}}$ can be achieved for the scalar DM--photon, DM--gluon, and DM--quark couplings $f_γ^{-1}$, $f_{g}^{-1}$, and $f_{\hat{m}}^{-1}$, respectively. In the low-mass region, the projected sensitivity to the scalar DM--photon coupling approaches the current constraints from equivalence-principle (EP) tests. These results demonstrate that Mössbauer-based techniques provide a promising and competitive approach for probing ultralight dark matter interactions with Standard Model particles.

hep-ph↗

arXiv2026

A Recursive Construction Improving the Lower Bound on the Shannon Capacity of $C_7$

We give a recursive reformulation and extension of the independent set of size $134753$ in $C_7^{10}$ constructed by N. Itty, C. D. Rosin, C. Carstensen, and D. Reichman (arXiv:2607.21517v1). We prove a product lemma that combines gadgets of different dimensions while preserving the required independence conditions. Starting from the size-$367$ independent set in $C_7^5$ of S. C. Polak and A. Schrijver (Information Processing Letters 143 (2019), 37-40), the construction gives an explicitly specified independent set in $C_7^{200}$. Consequently, \[ Θ(C_7)\geq 3.2587891539086910161967650155\ldots . \] An accompanying program verifies the finite assertions about the five-dimensional base gadget and performs the exact integer computations used in the recursion.

math.CO↗

arXiv2026

A data-driven stage-structured host-parasitoid model for optimizing Trichogramma interventions against soybean pod borer (Leguminivora glycinivorella) outbreaks

The soybean pod borer (Leguminivora glycinivorella) poses a severe threat to global soybean production.In this study, we developed a stage-structured host-parasitoid dynamic model that explicitly couples the holometabolous life cycle of the pest with the obligate egg-parasitism mechanism of Trichogramma wasps. Utilizing field monitoring data from Changchun, Jilin Province, key biological parameters were rigorously estimated via the Markov Chain Monte Carlo (MCMC) method.This calibration facilitated the establishment of a precise Economic Injury Level ($Q_{EIL}$) of 0.0389 individuals/$m^2$, based solely on the destructive larval stage. Through theoretical and numerical analyses of different intervention scenarios, we identified an optimal continuous release rate ($C^* = 2.645$) that efficiently suppresses the outbreak without causing wasteful parasitoid accumulation. Furthermore, simulations demonstrate that a 5-day impulsive release interval provides the optimal balance between strict pest suppression and field operational costs. This study bridges the gap between theoretical population dynamics and applied agricultural management, providing a directly applicable mathematical decision-making tool for the precise biological control of crop pests.

q-bio.PE↗

arXiv2026

CO Observations of Early-mid Stage Major Mergers in the MaNGA Survey

We present a study of the molecular gas in early-mid stage major-mergers, with a sample of 43 major-merger galaxy pairs selected from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey and a control sample of 195 isolated galaxies selected from the xCOLD GASS survey. Adopting kinematic asymmetry as a new effective indicator to describe the merger stage, we aim to study the role of molecular gas in the merger-induced star formation enhancement along the merger sequence of galaxy pairs. We obtain the molecular gas properties from CO observations with the James Clerk Maxwell Telescope (JCMT), Institut de Radioastronomie Milimetrique (IRAM) 30-m telescope, and the MASCOT survey. Using these data, we investigate the differences in molecular gas fraction ($f_{\rm H_{2}}$), star formation rate (SFR), star formation efficiency (SFE), molecular-to-atomic gas ratio ($M_{\rm H_{2}}/M_{\rm HI}$), total gas fraction ($f_{\rm gas}$), and the star formation efficiency of total gas (${\rm SFE_{gas}}$) between the pair and control samples. In the full pair sample, our results suggest the $f_{\rm H_{2}}$ of paired galaxies is significantly enhanced, while the SFE is comparable to that of isolated galaxies. We detect significantly increased $f_{\rm H_{2}}$ and $M_{\rm H_{2}}/M_{\rm HI}$ in paired galaxies at the pericenter stage, indicating an accelerated transition from atomic gas to molecular gas due to interactions. Our results indicate that the elevation of $f_{\rm H_{2}}$ plays a major role in the enhancement of global SFR in paired galaxies at the pericenter stage, while the contribution of enhanced SFE in specific regions requires further explorations through spatially resolved observations of a larger sample spanning a wide range of merger stages.

astro-ph.GA↗

arXiv2026

Read It Back: Pretrained MLLMs Are Zero-Shot Reward Models for Text-to-Image Generation

In this paper, we propose SpectraReward, a training-free reward function that turns pretrained MLLMs into off-the-shelf reward models for image-generation reinforcement learning. Instead of asking the MLLM to judge a generated image or answer decomposed verification questions, SpectraReward measures how well the original prompt can be recovered from the generated image through a single image-conditioned, teacher-forced forward pass. We use the average image-conditioned prompt log-likelihood as the reward, directly reusing the MLLM's pretrained image-text alignment ability without preference labels, reward-model fine-tuning. We further introduce Self-SpectraReward, a special case for unified multimodal models where the policy's own understanding branch serves as the reward model for its generation branch, forming a closed-loop self-improving framework without external reward models or external knowledge. Extensive experiments validate SpectraReward through a broad image-generation RL study covering two diffusion models, three RL algorithms, nine reward MLLM backbones from four MLLM families spanning 4B to 235B parameters, and five out-of-distribution text-to-image benchmarks. Results show that both SpectraReward and Self-SpectraReward significantly and consistently improve generation performance and outperform prior MLLM-derived reward training methods. Further analysis reveals that larger reward MLLMs are not always better, while Self-SpectraReward can match or surpass much larger external reward models, suggesting that reward-policy alignment is a key factor for effective image-generation RL. Project Page: https://huangrh99.github.io/SpectraReward/

cs.CV↗

arXiv2026

Rectilinear Matching to the Integer Grid in Nearly-Linear Time

Rectilinear matching to the integer grid asks to assign each of $n$ points in $\mathbb R^2$ to a distinct point of $\mathbb Z^2$, minimizing total $\ell_1$ movement. The main difficulty is that the target set is infinite: one must first identify a finite set of relevant grid points without losing optimality. We prove a geometric compression theorem for this infinite-target problem. In $O(n\log^2 n)$ time, we construct a set $\mathcal{C}$ of asymptotically optimal size $O(n)$ such that, simultaneously for every $p\in[1,\infty]$, some optimal $\ell_p$ assignment uses only points of $\mathcal{C}$. The construction is independent of the subsequent optimization algorithm and of the coordinate spread. For the rectilinear case, we combine this candidate set with a linear-size sparse network representation of $\ell_1$ distances. In the word-RAM model with $O(1)$-word dyadic coordinates and $O(\log n)$ fractional bits, a nearly-linear time minimum-cost flow algorithm then gives a randomized exact algorithm with expected running time $\widetilde O(n)$. This improves the standard $\widetilde O(n^2)$ approach. Combined with existing finite geometric matching algorithms, the same candidate set also gives an $\widetilde O(n\sqrt n\log(1/\varepsilon))$-time $(1+\varepsilon)$ approximation for every fixed integer $p\ge1$.

cs.CG↗

arXiv2026

SutureFormer: Learning Surgical Trajectories via Goal-conditioned Offline RL in Pixel Space

Predicting surgical needle trajectories from endoscopic video is critical for robot-assisted suturing, enabling anticipatory planning, real-time guidance, and safer motion execution. Existing methods that directly learn motion distributions from visual observations tend to overlook the sequential dependency among adjacent motion steps. Moreover, sparse waypoint annotations often fail to provide sufficient supervision, further increasing the difficulty of supervised or imitation learning methods. To address these challenges, we formulate image-based needle trajectory prediction as a sequential decision-making problem, in which the needle tip is treated as an agent that moves step by step in pixel space. This formulation naturally captures the continuity of needle motion and enables the explicit modeling of physically plausible pixel-wise state transitions over time. From this perspective, we propose SutureFormer, a goal-conditioned offline reinforcement learning framework that leverages sparse annotations to dense reward signals via cubic spline interpolation, encouraging the policy to exploit limited expert guidance while exploring plausible future motion paths. SutureFormer encodes variable-length clips using an observation encoder to capture both local spatial cues and long-range temporal dynamics, and autoregressively predicts future waypoints through actions composed of discrete directions and continuous magnitudes. To enable stable offline policy optimization from expert demonstrations, we adopt Conservative Q-Learning with Behavioral Cloning regularization. Experiments on a new kidney wound suturing dataset containing 1,158 trajectories from 50 patients show that SutureFormer reduces Average Displacement Error by 58.6% compared with the strongest baseline, demonstrating the effectiveness of modeling needle trajectory prediction as pixel-level sequential action learning.

cs.RO↗

arXiv2026

Performance Analysis and Optimization for Laser-Phase-Noise based Quantum Random Number Generation

The quantum random number generation based on laser phase noise, which is featured with high random number generation rate and ease for photonic integration, has been extensively investigated and demonstrated. Despite these advancements, a theoretical model to achieve optimal performance in terms of maximizing the random number generation rate is still incomplete. In this work, a comprehensive physical model for this scheme is introduced to accurately predict the power spectrum of entropy source and probability distribution of raw data, based on which the entropy source bandwidth and extractable randomness can be accordingly estimated and thus the system performance can be quantitatively evaluated and optimized. The model is sufficiently validated through both simulation and experiment with significant agreement under various typical setups. Furthermore, our proposal enables the proactive design of experimental parameters to achieve optimal system performance, which is crucial for the design and practical implementation of photonics integrated quantum random number generations.

quant-ph↗

arXiv2026

CubifyGS: Object-Centric 3D Gaussian Splatting for Lifelong Dynamic Scene Maintenance

Lifelong scene mapping under rigid object rearrangement remains a fundamental challenge in robotics. While 3D Gaussian Splatting (3DGS) enables high-fidelity modeling, primitive-level updates often cause persistent ghosting and slow recovery. We propose CubifyGS, an object-level mapping framework that shifts dynamic maintenance from passive re-optimization to active asset management. CubifyGS models movable instances as reusable Gaussian assets, detects object appearance and disappearance, and updates maps through asset retrieval, rigid transformation, and explicit pruning rather than reconstruction from scratch. To address geometric voids and local photometric mismatch after such edits, we further propose an event-triggered adaptive optimization strategy that focuses computation on affected regions. We validate our approach on a newly constructed high-fidelity dynamic benchmark, demonstrating that CubifyGS improves artifact suppression and maintenance efficiency over representative reproducible baselines in the evaluated object-rearrangement setting.

cs.RO↗

arXiv2026

Parametric-Resonance Production of QCD Axions

Dark matter axion production can be significantly enhanced through a generic cosmological mechanism: primordial temperature fluctuations periodically modulate the axion mass during the QCD phase transition, thereby triggering parametric resonance in axion field evolution. This interplay between the resonance and the misalignment mechanism moves the predicted axion mass window for the observed dark matter abundance to $10^{-4}-10^{-3} \, \text{eV}$, shifting the preferred mass to previously unexplored higher ranges.

hep-ph↗

arXiv2026

Nonlinear subwavelength resonances and bound states in the continuum in metascreens

This paper establishes a mathematical framework for nonlinear subwavelength resonances and bound states in the continuum (BIC) in an acoustic metascreen with a cubic Kerr nonlinearity. We first use the quasiperiodic Dirichlet-to-Neumann operator to reduce the open resonance problem to an interior nonlinear variational problem. We then decompose the function space in which the variational problem is posed as the direct sum of two spaces and project the variational problem onto these two subspaces. Solving the projected equations successively yields a finite-dimensional nonlinear resonance equation with controlled remainders. We next apply the implicit function theorem near simple capacitance modes. This proves the existence and asymptotic expansions of linear subwavelength resonance branches and their small-amplitude nonlinear continuations. Finally, reflection symmetry gives a classification of the subwavelength branches. We characterize the symmetric resonance branches and prove that antisymmetric branches are exact BICs in both the linear problem and the nonlinear problem.

math.AP↗

arXiv2026

A superconducting surface-code processor with lattice-surgery logical operations

Fault-tolerant logical operations are fundamental for scalable quantum computation. Here, we report the experimental realization of lattice-surgery operations between a pair of distance-three surface-code logical qubits on a planar superconducting processor. During repeated syndrome extraction cycles, the logical qubits exhibit per-cycle error rates of $0.0365(2)$ and $0.0282(1)$, respectively, after leakage events are rejected. By leveraging joint initialization and lattice splitting, we deterministically prepare a logical Bell state, confirming genuine bipartite entanglement via the error-corrected logical state fidelity. We further execute a two-qubit Deutsch-Jozsa algorithm at the logical level to demonstrate algorithmic utility in a fault-tolerant framework. Finally, to achieve universal control, we implement magic-state injection and gate teleportation to realize continuous non-Clifford rotations about the logical $X$ axis. For the logical $R_{X}(π/4)$ gate, we achieve a logical gate fidelity of $0.943_{-9}^{+10}$ conditioned on the absence of detected errors. These results establish lattice surgery as a practical and versatile paradigm for logical computation in near-term surface-code architectures, representing a critical milestone toward scalable fault-tolerant quantum advantage in superconducting circuits.

quant-ph↗