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Researcher profile

Yan Xu

Yan Xu contributes to research discovery and scholarly infrastructure.

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Computer Visionnucl-th

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Published work

4 published item(s)

preprint2026arXiv

A Study on the Triggering of Nucleonic Direct Urca Processes in Neutron Stars of Specific Masses and Their Hyperon Dependence

This work aims to analyze how hyperons affect neutrino radiation properties in nucleonic direct URCA processes, expecting to provide useful references for finding evidence of the existence of hyperons in astronomical observations. This analysis is carried out using the GM1 and NL3 parameter sets under the SU(6) and SU(3) flavor symmetries in the relativistic mean field theory framework. Combined with the inferred mass and radius values of PSRs J1231-1411, J0030+0451, and J0740+6620, our results show that nucleonic direct Urca processes are absent in PSR J1231-1411 due to momentum conservation violation. In hyperon-containing PSR J0030+0451 (NL3 parameter set), the nucleonic direct Urca processes involving $e^{-}$/ $μ^{-}$ would occur. A large inferred mass span induces hyperon fraction variations, affecting neutrino emissivity. If the inferred mass of PSR J0030+0451 exceeds approximately 1.8 $M_{\odot}$, the neutrino luminosity of the nucleonic direct Urca processes under the SU(3) flavor symmetry remains nearly the same as that in npe$μ$ matter, without depending on hyperons. However, it exhibits an obvious hyperon dependence under the SU(6) spin-flavor symmetry. For hyperon-containing J0740+6620, the nucleonic direct Urca processes under the SU(3) flavor symmetry in GM1 parameter set predicts faster neutrino luminosity decline with hyperonic fraction than npe$μ$ matter, and under the SU(6) spin-flavor symmetry in NL3 parameter set it shows monotonic decreasing trend. The research shows that hyperonic fraction significantly affect the neutrino radiation properties of the nucleonic direct URCA processes in neutron stars. Different-mass pulsars (e.g., PSRs J1231-1411, J0030+0451, J0740+6620) exhibit the distinct nucleonic direct URCA processes behaviors, dependent on inferred masses/radii, parameter sets, and theoretical models.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

CTIS-QA: Clinical Template-Informed Slide-level Question Answering for Pathology

In this paper, we introduce a clinical diagnosis template-based pipeline to systematically collect and structure pathological information. In collaboration with pathologists and guided by the the College of American Pathologists (CAP) Cancer Protocols, we design a Clinical Pathology Report Template (CPRT) that ensures comprehensive and standardized extraction of diagnostic elements from pathology reports. We validate the effectiveness of our pipeline on TCGA-BRCA. First, we extract pathological features from reports using CPRT. These features are then used to build CTIS-Align, a dataset of 80k slide-description pairs from 804 WSIs for vision-language alignment training, and CTIS-Bench, a rigorously curated VQA benchmark comprising 977 WSIs and 14,879 question-answer pairs. CTIS-Bench emphasizes clinically grounded, closed-ended questions (e.g., tumor grade, receptor status) that reflect real diagnostic workflows, minimize non-visual reasoning, and require genuine slide understanding. We further propose CTIS-QA, a Slide-level Question Answering model, featuring a dual-stream architecture that mimics pathologists' diagnostic approach. One stream captures global slide-level context via clustering-based feature aggregation, while the other focuses on salient local regions through attention-guided patch perception module. Extensive experiments on WSI-VQA, CTIS-Bench, and slide-level diagnostic tasks show that CTIS-QA consistently outperforms existing state-of-the-art models across multiple metrics. Code and data are available at https://github.com/HLSvois/CTIS-QA.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

HyperVision: A Channel-Adaptive Ground-Based Hyperspectral Vision Pre-trained Backbone

While hyperspectral imaging provides rich spatial-spectral information across hundreds of narrow wavelength bands for precise material identification, ground-based hyperspectral pre-trained backbones remain absent, constrained by varying spectral configurations across sensors, the scarcity and inconsistency of labels, and the limited scale and scene diversity of existing datasets. To address these challenges and enable universal perception, we propose HyperVision, the first ground-based hyperspectral pre-trained backbone. First, to handle varying spectral configurations, HyperVision adopts a channel-adaptive dynamic embedding mechanism to map heterogeneous inputs into a unified token space. Second, to address the scarcity and inconsistency of labels, we introduce a multi-source pseudo-labeling method that fuses semantic representations from both spatial structures generated by SAM2 and fine-grained spectral material information extracted by HyperFree. Third, to compensate for limited dataset scale and enrich scene diversity, a cross-modal knowledge distillation mechanism is utilized to transfer rich semantic representations from a pre-trained RGB vision model to our hyperspectral backbone. Pre-trained on a collection of 15k images from 26 diverse ground-based datasets, HyperVision demonstrates exceptional generalization. Requiring only efficient head-only adaptation without adjusting backbone parameters, it achieves state-of-the-art performance compared to task-specific methods across three downstream tasks under varying sensor configurations, yielding up to a 16.3% relative improvement in hyperspectral semantic segmentation $\mathrm{Acc}_{\mathrm{M}}$, a 2.1% relative gain in object tracking AUC, and a 35.5% reduction in salient object detection MAE. The source code and pre-trained model will be publicly available at https://github.com/lronkitty/HyperVision .

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

MMDeepResearch-Bench: A Benchmark for Multimodal Deep Research Agents

Deep Research Agents (DRAs) generate citation-rich reports via multi-step search and synthesis, yet existing benchmarks mainly target text-only settings or short-form multimodal QA, missing end-to-end multimodal evidence use. We introduce MMDeepResearch-Bench (MMDR-Bench), a benchmark of 140 expert-crafted tasks across 21 domains, where each task provides an image-text bundle to evaluate multimodal understanding and citation-grounded report generation. Compared to prior setups, MMDR-Bench emphasizes report-style synthesis with explicit evidence use, where models must connect visual artifacts to sourced claims and maintain consistency across narrative, citations, and visual references. We further propose a unified, interpretable evaluation pipeline: Formula-LLM Adaptive Evaluation (FLAE) for report quality, Trustworthy Retrieval-Aligned Citation Evaluation (TRACE) for citation-grounded evidence alignment, and Multimodal Support-Aligned Integrity Check (MOSAIC) for text-visual integrity, each producing fine-grained signals that support error diagnosis beyond a single overall score. Experiments across 25 state-of-the-art models reveal systematic trade-offs between generation quality, citation discipline, and multimodal grounding, highlighting that strong prose alone does not guarantee faithful evidence use and that multimodal integrity remains a key bottleneck for deep research agents.

0 citations0 reviewsNo code linkedOpen access