arXiv subjects

Yu Xu

Publications and source records attributed to Yu Xu.

At least 37 records · Page 2Linked to original sources

arXiv2026

Nested Multilevel Monte Carlo with Preintegration for Efficient Risk Estimation

Nested Monte Carlo is widely used for risk estimation, but its efficiency is limited by the discontinuity of the indicator function and high computational cost. This paper proposes a nested Multilevel Monte Carlo (MLMC) method combined with preintegration for efficient risk estimation. We first use preintegration to integrate out one outer random variable, which effectively handles the discontinuity of the indicator function, then we construct the MLMC estimator with preintegration to reduce the computational cost. Our theoretical analysis proves that the strong convergence rate of the MLMC combined with preintegration reaches -1, compared with -1/2 for the standard MLMC. Consequently, we obtain a nearly optimal computational complexity. Besides, our method can also handle the high-kurtosis phenomenon caused by indicator functions. Numerical experiments verify that the smoothed MLMC with preintegration outperforms the standard MLMC and the optimal computational cost can be attained. Combining our method with quasi-Monte Carlo further improves its performance in high dimensions. Keywords: Nested simulation, Multilevel Monte Carlo, Risk estimation, Preintegration

math.NA↗

arXiv2026

TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts

Unified image generation and editing models suffer from severe task interference in dense diffusion transformers architectures, where a shared parameter space must compromise between conflicting objectives (e.g., local editing v.s. subject-driven generation). While the sparse Mixture-of-Experts (MoE) paradigm is a promising solution, its gating networks remain task-agnostic, operating based on local features, unaware of global task intent. This task-agnostic nature prevents meaningful specialization and fails to resolve the underlying task interference. In this paper, we propose a novel framework to inject semantic intent into MoE routing. We introduce a Hierarchical Task Semantic Annotation scheme to create structured task descriptors (e.g., scope, type, preservation). We then design Predictive Alignment Regularization to align internal routing decisions with the task's high-level semantics. This regularization evolves the gating network from a task-agnostic executor to a dispatch center. Our model effectively mitigates task interference, outperforming dense baselines in fidelity and quality, and our analysis shows that experts naturally develop clear and semantically correlated specializations.

cs.CV↗

arXiv2026

Expansion of Momentum Space and Full 2$π$ Solid Angle Photoelectron Collection in Laser-Based Angle-Resolved Photoemission Spectroscopy by Applying Sample Bias

Angle-resolved photoemission spectroscopy (ARPES) directly probes the energy and momentum of electrons in quantum materials, but conventional setups capture only a small fraction of the full 2$π$ solid angle. This limitation is acute in laser-based ARPES, where the low photon energy restricts momentum space despite ultrahigh resolution. Here we present systematic studies of bias ARPES, where applying a sample bias expands the accessible momentum range and enables full 2$π$ solid angle collection in two dimension using our 6.994 eV laser source. An analytical conversion relation is established and validated to accurately map the detector angle to the emission angle and the electron momentum in two dimensions. A precise approach is developed to determine the sample work function which is critical in the angle-momentum conversion of the bias ARPES experiments. Energy and angular resolutions are preserved under biases up to 100 V, and minimizing beam size is shown to be crucial. The technique is effective both near normal and off-normal geometries, allowing flexible Brillouin zone access with lower biases. Bias ARPES thus elevates laser ARPES to a new level, extending momentum coverage while retaining high resolution, and is applicable across a broad photon-energy range.

cond-mat.supr-con↗

arXiv2026

Generative Visual Chain-of-Thought for Image Editing

Existing image editing methods struggle to perceive where to edit, especially under complex scenes and nuanced spatial instructions. To address this issue, we propose Generative Visual Chain-of-Thought (GVCoT), a unified framework that performs native visual reasoning by first generating spatial cues to localize the target region and then executing the edit. Unlike prior text-only CoT or tool-dependent visual CoT paradigms, GVCoT jointly optimizes visual tokens generated during the reasoning and editing phases in an end-to-end manner. This way fosters the emergence of innate spatial reasoning ability and enables more effective utilization of visual-domain cues. The main challenge of training GCVoT lies in the scarcity of large-scale editing data with precise edit region annotations; to this end, we construct GVCoT-Edit-Instruct, a dataset of 1.8M high-quality samples spanning 19 tasks. We adopt a progressive training strategy: supervised fine-tuning to build foundational localization ability in reasoning trace before final editing, followed by reinforcement learning to further improve reasoning and editing quality. Finally, we introduce SREdit-Bench, a new benchmark designed to comprehensively stress-test models under sophisticated scenes and fine-grained referring expressions. Experiments demonstrate that GVCoT consistently outperforms state-of-the-art models on SREdit-Bench and ImgEdit. We hope our GVCoT will inspire future research toward interpretable and precise image editing.

cs.CV↗

arXiv2026

Half-year Evolution of a Decaying Solar Active Region and Peripheral Dimming Regions

Using multi-wavelength observations from the Solar Dynamics Observatory (SDO), we investigated the six-month decay process of the solar active region NOAA AR 12738 from April to October 2019. We systematically analyzed the region's evolution by examining extreme ultraviolet (EUV) intensity variations, quantifying magnetic flux diffusion, and investigating thermodynamic changes via Differential Emission Measure (DEM) analysis. This study presents the first long-term tracking of a peripheral dimming region (dark moat), revealing its continuous areal decrease over time. DEM results reveal cooling plasma signatures and thermal restructuring, with the dimming region exhibiting a distinct temperature deficit in range 10$^{5.5}$ -- 10$^{5.9}$~K. Potential field extrapolation identifies two dominant magnetic configurations: low-lying loops with cool plasma ($<$10$^{5.5}$ K), and high-arching structures connecting to the AR core, contributing to localized emission reduction. We found that the dimming is dominated by high-lying loops extending from the AR core, which are heated to temperatures above the main response of the 171~Å passband ($>$ 10$^{5.8}$ K), consequently lacking plasma at the typical 10$^{5.8}$~K formation temperature. The thermal deficit, not just the absence of material, is the key driver of the reduced emission. Our results demonstrate that long-duration dimming provides a valuable diagnostic for understanding active region decay, thermal evolution, and coronal magnetic restructuring.

astro-ph.SR↗

arXiv2026

Locomo-Plus: Beyond-Factual Cognitive Memory Evaluation Framework for LLM Agents

Long-term conversational memory is a core capability for LLM-based dialogue systems, yet existing benchmarks and evaluation protocols primarily focus on surface-level factual recall. In realistic interactions, appropriate responses often depend on implicit constraints such as user state, goals, or values that are not explicitly queried later. To evaluate this setting, we introduce \textbf{LoCoMo-Plus}, a benchmark for assessing cognitive memory under cue--trigger semantic disconnect, where models must retain and apply latent constraints across long conversational contexts. We further show that conventional string-matching metrics and explicit task-type prompting are misaligned with such scenarios, and propose a unified evaluation framework based on constraint consistency. Experiments across diverse backbone models, retrieval-based methods, and memory systems demonstrate that cognitive memory remains challenging and reveals failures not captured by existing benchmarks. Our code and evaluation framework are publicly available at: https://github.com/xjtuleeyf/Locomo-Plus.

cs.CL↗

arXiv2026

Re-Align: Structured Reasoning-guided Alignment for In-Context Image Generation and Editing

In-context image generation and editing (ICGE) enables users to specify visual concepts through interleaved image-text prompts, demanding precise understanding and faithful execution of user intent. Although recent unified multimodal models exhibit promising understanding capabilities, these strengths often fail to transfer effectively to image generation. We introduce Re-Align, a unified framework that bridges the gap between understanding and generation through structured reasoning-guided alignment. At its core lies the In-Context Chain-of-Thought (IC-CoT), a structured reasoning paradigm that decouples semantic guidance and reference association, providing clear textual target and mitigating confusion among reference images. Furthermore, Re-Align introduces an effective RL training scheme that leverages a surrogate reward to measure the alignment between structured reasoning text and the generated image, thereby improving the model's overall performance on ICGE tasks. Extensive experiments verify that Re-Align outperforms competitive methods of comparable model scale and resources on both in-context image generation and editing tasks.

cs.CV↗

arXiv2025

Randomized Quasi-Monte Carlo and Importance Sampling for Super-Fast Growing Functions with Applications to Finance

Many problems can be formulated as high-dimensional integrals of discontinuous functions that exhibit significant boundary growth, challenging the error analysis and applications of randomized quasi-Monte Carlo (RQMC) methods. This paper studies RQMC methods for super-fast growing functions satisfying generalized exponential growth conditions, with a special focus on financial derivative pricing. The main contribution of this paper is threefold. First, by combining RQMC with importance sampling (IS), we derive a new error bound for a class of integrands, whose values and derivatives are bounded by the critical growth function $e^{A|\boldsymbol{x}|^2}$ with $A = 1/2$. This result extends the existing results in the literature, which are limited to the case $A < 1/2$. We demonstrate that by imposing a light-tailed condition on the proposal distribution of IS, RQMC can achieve an error rate of $O(n^{-1 + ε})$ with a sample size n and an arbitrarily small $ε>0$. Second, we verify that the Gaussian proposals used in Optimal Drift Importance Sampling (ODIS) satisfy the required light-tailed condition, providing a rigorous theoretical guarantees for RQMC-ODIS in critical growth scenarios. Third, for discontinuous integrands from finance, we prove that the integrands after preintegration satisfy the exponential growth condition. This ensures that the preintegrated functions can be seamlessly incorporated into our RQMC-IS framework. Numerical experiments on financial derivative pricing validate our theory, showing that the RQMC-IS with preintegration is effective in handling problems with discontinuous payoffs, successfully achieving the expected convergence rates.

math.NA↗

arXiv2025

Algorithmic Quantum Simulations of Quantum Thermodynamics

Characterizing quantum phases-of-matter at finite-temperature is essential for understanding complex materials and large-scale thermodynamic phenomena. Here, we develop algorithmic protocols for simulating quantum thermodynamics on quantum hardware through quantum kernel function expansion (QKFE), producing the free energy as an analytic function of temperature with uniform convergence. These protocols are demonstrated by simulating transverse field Ising and XY models with superconducting qubits. In both analogue and digital implementations of the QKFE algorithms, we exhibit quantitative agreement of our quantum simulation experiments with the exact results. Our approach provides a general framework for computing thermodynamic potentials on programmable quantum devices, granting access to key thermodynamic properties such as entropy, heat capacity and criticality, with far-reaching implications for material design and drug development.

quant-ph↗

arXiv2025

Conceptual Design of the Muonium-to-Antimuonium Conversion Experiment (MACE)

The spontaneous conversion of muonium to antimuonium is one of the interesting charged lepton flavor violation phenomena offering a sensitive probe of potential new physics and serving as a tool to constrain the parameter space beyond the Standard Model. The Muonium-to-Antimuonium Conversion Experiment (MACE) is designed to utilize a high-intensity muon beam, a Michel electron magnetic spectrometer, a positron transport system, and a positron detection system, to either discover or constrain this rare process with a conversion probability of $\mathcal{O}(10^{-13})$. This article presents an overview of the theoretical framework as well as a detailed description of the experimental design for the search for muonium-to-antimuonium conversion.

hep-ex↗

arXiv2025

Search for light Dark Sectors with GeV Muon Beams

Sub-GeV light dark matter often requires new light mediators, such as a dark $Z$ boson in the $L_μ- L_τ$ gauge theory. We study the search potential for such a $Z^\prime$ boson via the process $μe^- \to μe^- X$, with $X$ decaying invisibly, in a muon on-target experiment using a high-intensity 1-10 GeV muon beam from facilities such as HIAF-HIRIBL. Events are identified by the scattered muon and electron from the target using silicon strip detectors in a single-station telescope system. Backgrounds are suppressed through a trained boosted decision tree (BDT) classifier, and activity in downstream subdetectors remains low. This approach can probe a $Z^\prime$ boson in the 10 MeV mass range with improved sensitivity. Nearly three orders of magnitude improvement is achievable with a full multi-telescope station system employing a 160 GeV muon beam at CERN, such as in the MUonE experiment.

hep-ph↗

arXiv2025

The Exospace Weather Frontier

Space weather is among the most powerful and least understood forces shaping planetary atmospheres. In our Solar System, we observe its effects directly: atmospheric escape, chemical disruption, and spectacular auroral displays. Yet for exoplanets, we lack the tools and data to comprehensively assess the impacts of space weather, especially invisible elements like stellar winds, coronal mass ejections, energetic particles, and variable interplanetary magnetic fields. This problem lies at the intersection of four key fields: heliophysics, planetary science, astrobiology, and astrophysics. In 2023--2025, experts from these four fields convened at the W. M. Keck Institute for Space Studies to explore pathways for advancing the study of exospace weather. Organizing the subject into five core themes -- planets and their stellar particle environments, stellar magnetism and space weather modeling, quasi-steady stellar winds, transient events, and programmatic pathways -- our team synthesized concepts from across relevant fields and identified a wide array of opportunities for progress. This report is the product of that effort. It assembles cross-disciplinary knowledge; highlights outstanding theoretical challenges; explores promising innovations in observation, modeling, methodology, and instrumentation; and makes recommendations for accelerating community-wide progress. Together, these lay out a path to transforming the challenging, yet tractable problem of exospace weather into a foundational element of our understanding exoplanetary systems, and our own Solar System, in their entirety.

astro-ph.IM↗

arXiv2025

High-Precision Physics Experiments at Huizhou Large-Scale Scientific Facilities

In response to the capabilities presented by the High-Intensity Heavy Ion Accelerator Facility (HIAF) and the Accelerator-Driven Subcritical System (CiADS), as well as the proposed Chinese Advanced Nuclear Physics Research Facility (CNUF), we are assembling a consortium of experts in relevant discipline--both domestically and internationally--to delineate high-precision physics experiments that leverage the state-of-the-art research environment afforded by CNUF. Our focus encompasses six primary domains of inquiry: hadron physics--including endeavors such as the super eta factory and investigations into light hadron structures; muon physics; neutrino physics; neutron physics; the testing of fundamental symmetries; and the exploration of quantum effects within nuclear physics, along with the utilization of vortex accelerators. We aim to foster a well-rounded portfolio of large, medium, and small-scale projects, thus unlocking new scientific avenues and optimizing the potential of the Huizhou large scientific facility. The aspiration for international leadership in scientific research will be a guiding principle in our strategic planning. This initiative will serve as a foundational reference for the Institute of Modern Physics in its strategic planning and goal-setting, ensuring alignment with its developmental objectives while striving to secure a competitive edge in technological advancement. Our ambition is to engage in substantive research within these realms of high-precision physics, to pursue groundbreaking discoveries, and to stimulate progress in China's nuclear physics landscape, positioning Huizhou as a preeminent global hub for advanced nuclear physics research.

hep-ph↗

arXiv2025

Mendelian randomization in a multi-ancestry world: reflections and practical advice

Many Mendelian randomization (MR) papers have been conducted only in people of European ancestry, limiting transportability of results to the global population. Expanding MR to diverse ancestry groups is essential to ensure equitable biomedical insights, yet presents analytical and conceptual challenges. This review examines the practical challenges of MR analyses beyond the European only context, including use of data from multi-ancestry, mismatched ancestry, and admixed populations. We explain how apparent heterogeneity in MR estimates between populations can arise from differences in genetic variant frequencies and correlation patterns, as well as from differences in the distribution of phenotypic variables, complicating the detection of true differences in the causal pathway. We summarize published strategies for selecting genetic instruments and performing analyses when working with limited ancestry-specific data, discussing the assumptions needed in each case for incorporating external data from different ancestry populations. We conclude that differences in MR estimates by ancestry group should be interpreted cautiously, with consideration of how the identified differences may arise due to social and cultural factors. Corroborating evidence of a biological mechanism altering the causal pathway is needed to support a conclusion of differing causal pathways between ancestry groups.

q-bio.PE↗

arXiv2025

UniMoE-Audio: Unified Speech and Music Generation with Dynamic-Capacity MoE

Recent advances in unified multimodal models indicate a clear trend towards comprehensive content generation. However, the auditory domain remains a significant challenge, with music and speech often developed in isolation, hindering progress towards universal audio synthesis. This separation stems from inherent task conflicts and severe data imbalances, which impede the development of a truly unified audio generation model. To address this challenge, we propose UniMoE-Audio, a unified speech and music generation model within a novel Dynamic-Capacity Mixture-of-Experts (MoE) framework. Architecturally, UniMoE-Audio introduces a Top-P routing strategy for dynamic expert number allocation, and a hybrid expert design comprising routed experts for domain-specific knowledge, shared experts for domain-agnostic features, and null experts for adaptive computation skipping. To tackle data imbalance, we introduce a three-stage training curriculum: 1) Independent Specialist Training leverages original datasets to instill domain-specific knowledge into each "proto-expert" without interference; 2) MoE Integration and Warmup incorporates these specialists into the UniMoE-Audio architecture, warming up the gate module and shared expert using a subset of balanced dataset; and 3) Synergistic Joint Training trains the entire model end-to-end on the fully balanced dataset, fostering enhanced cross-domain synergy. Extensive experiments show that UniMoE-Audio not only achieves state-of-the-art performance on major speech and music generation benchmarks, but also demonstrates superior synergistic learning, mitigating the performance degradation typically seen in naive joint training. Our findings highlight the substantial potential of specialized MoE architecture and curated training strategies in advancing the field of universal audio generation. Homepage: https://mukioxun.github.io/Uni-MoE-site/home.html

cs.SD↗

arXiv2025

Far beyond the Sun: III. The magnetic cycle of $\boldsymbolι$ Horologii

We present a comprehensive investigation of the magnetic cycle of the young, active solar analogue $ι$ Horologii ($ι$ Hor) based on intensive spectropolarimetric monitoring using HARPSpol. Over a nearly three-year campaign, the technique of Zeeman-Doppler Imaging (ZDI) was used to reconstruct 18 maps of the large-scale surface magnetic field of the star. These maps trace the evolution of the magnetic field morphology over approximately 139 stellar rotations. Our analysis uncovers pronounced temporal evolution, including multiple polarity reversals and changes in field strength and geometry. We examine the evolution of the poloidal and toroidal field components, with the toroidal component showing strong modulation in concert with the chromospheric activity. Furthermore, for the first time, we reconstruct stellar magnetic butterfly diagrams which are used to trace the migration of large-scale magnetic features across the stellar surface, determining a magnetic polarity reversal timescale of roughly 100 rotations ($\sim773$ d). In addition, by tracking the field-weighted latitudinal positions, we obtain the first estimates of the large-scale flow properties on a star other than the Sun, identifying possible pole-ward and equator-ward drift speeds for different field polarities. These results provide critical insights into the dynamo processes operating in young solar-type stars and offer a direct comparison with the solar magnetic cycle.

astro-ph.SR↗

arXiv2025

Fabrication and characterization of AlMn alloy superconducting films for 0vbb experiments

Neutrinoless double-beta decay (0vbb) experiments constitute a pivotal probe for elucidating the characteristics of neutrinos and further discovering new physics. Compared to the neutron transmutation doped germanium thermistors (NTD-Ge) used in 0vbb experiments such as CUORE, transition edge sensors (TES) theoretically have a relatively faster response time and higher energy resolution. These make TES detectors good choice for next generation 0vbb experiments. In this paper, AlMn alloy superconducting films, the main components of TES, were prepared and studied. The relationship between critical temperature (Tc) and annealing temperature was established, and the impact of magnetic field on Tc was tested. The experimental results demonstrate that the Tc of AlMn film can be tuned in the required range of 10 - 20 mK by using the above methods, which is a key step for the application of AlMn TES in 0vbb experiment. In the test range, the Tc of AlMn film is sensitive to out-of-plane magnetic field but not to the in-plane magnetic field. Furthermore, we find that a higher annealing temperature results in a more uniform distribution of Mn ions in depth, which opens a new avenue for elucidating the underlying mechanism for tuning Tc.

hep-ex↗

arXiv2025

Fabrication and Characterization of X-ray TES Detectors Based on Annular AlMn Alloy Films

AlMn alloy flms are widely fabricated into superconducting transition edge sensors (TESs) for the detection of cosmic microwave background radiation. However, the application in X-ray or gamma-ray detection based on AlMn TES is rarely reported. In this study, X-ray TES detectors based on unique annular AlMn flms are devel-oped. The fabrication processes of TES detectors are introduced in detail. The char-acteristics of three TES samples are evaluated in a dilution refrigerator. The results demonstrate that the I-V characteristics of the three annular TES detectors are highly consistent. The TES detector with the smallest absorber achieved the best energy resolution of 11.0 eV @ 5.9 keV, which is inferior to the theoretical value. The dis-crepancy is mainly attributed to the larger readout electronics noise than expected.

physics.ins-det↗