arXiv subjects

Haoyang Yuan

Publications and source records attributed to Haoyang Yuan.

9 recordsLinked to original sources

arXiv2026

Torsion growth of rational elliptic curves over $\Z_p$-extensions of quadratic fields

Let $E/\Q$ be an elliptic curve and let $\widetilde K_p$ be the compositum of all $\Z_p$-extensions of a quadratic field $K$. We prove that $E(\widetilde K_p)_{\tors}=E(K)_{\tors}$ for $p\geq5$. For $p=3$ and imaginary quadratic $K\neq\Q(\sqrt{-3})$, torsion on each extension is determined by its intersections with the cyclotomic extension and the $2$-division field. Over $\Q(\sqrt{-3})$, we construct infinitely many non-CM curves with full $3$-torsion in the first anticyclotomic layer and compute the $3$-primary torsion on every slope for eight CM curves. For $p=2$, we bound the odd-primary torsion and exclude all primes greater than $7$. We also give uniform bounds for non-CM primary torsion and correct two assertions in Li's preprint about noncyclotomic $\Z_3$-extensions.

math.NT↗

arXiv2026

A Lang-Trotter Problem for Non-Geometric Quadratic Inductions

Let $K/\mathbb Q$ be an imaginary quadratic extension and $p$ an odd prime. Write $ρ=\operatorname{Ind}_{G_K}^{G_{\mathbb Q}}χ$, where $E/\mathbb Q_p$ is a finite extension and $χ:G_K\to\mathcal O_E^\times$ is a continuous character. For a fixed $r\in\mathbb Z\setminus\{0\}$, let $π_{ρ,r}(X)$ denote the number of rational primes $\ell\le X$ such that $ρ$ is unramified at $\ell$ and $\operatorname{tr}ρ(\operatorname{Frob}_\ell)=r$. Let $a,b$ be the two weights of $χ$ at $p$. We prove that if $(a,b)\notin\mathbb Q^2$, then $π_{ρ,r}(X)\ll_{ρ,r,\varepsilon}X^\varepsilon$ for every $\varepsilon>0$, while if $(a,b)\in\mathbb Q^2\setminus\mathbb Z^2$, then only finitely many such primes occur. These bounds are substantially sparser than the classical CM Lang--Trotter scale. The main input in the non-rational case is a rigidity theorem for algebraic curves in the $p$-adic analytic trace locus, combined with rigid-analytic Pila--Wilkie counting; the rational non-integral case is treated by a local ramification argument.

math.NT↗

arXiv2026

Chang'e 7 Lunar Lander Optical Camera-Telescope: Optical Astronomy from the Moon

We report the design and manufacture of a new, lightweight, wide-field, optical camera-telescope on board the Chang'e 7 lunar mission (launched in August 2026 and due for lunar touchdown in late November 2026). The camera is capable of static, panchromatic imagery within a $420-696~nm$ optical wavelength range. The camera was designed and built under the small lunar astronomy observation station program of the Chinese National Space Agency (CNSA) as a collaboration between the International Lunar Observatory Association of Hawaii (ILOA), the Laboratory for Space Research (LSR) of the University of Hong Kong (HKU) and the Beijing Institute of Space Mechanics and Electricity (BISME). The camera has been built to meet science goals of the mission for sustainable astronomical operation over a large range of temperatures from the Moon's south pole. We report on the design and ground based preliminary performance, together with an analysis of the camera's simulated output to indicate the range of astronomical observations possible from the lunar surface given the camera's limited sensitivity and angular resolution given the modest aperture and wide field of view.

astro-ph.IM↗

arXiv2026

Understand Before Detect: Vision--Language Learning for Omni-Domain Infrared Small Target Detection

Omni-domain infrared small target (IRST) detection is crucial for infrared surveillance, yet remains challenging due to heterogeneous imaging domains and inconsistent target characteristics. Previous deep learning-based methods have been developed for visual-only paradigms and achieved promising performance on domain-specific tasks. However, existing methods follow the task-specific supervised learning paradigm. This paradigm simplifies the full-scene infrared observations to sparse target supervision, discarding the semantics that remain invariant across heterogeneous domains. Consequently, detection performance suffers substantially under domain shifts. To handle this issue, we introduce \textbf{``understand before detect''}, a paradigm that formulates omni-domain IRST detection as an understanding-driven process, where holistic infrared target understanding precedes precise detection. Building on this paradigm, we propose \textbf{JinSight}, which first develops holistic IRST understanding through language supervision and then transfers the learned cross-domain representations to precise small-target detection. By grounding infrared representations in language semantics, JinSight enables a single model to generalize across heterogeneous infrared domains. We then introduce Latent Semantic Interaction (LSI), which exchanges language-aligned global semantics with fine-grained spatial features in a compact low-rank space. To address the lack of multimodal omni-domain IRST benchmarks, we build \textbf{OmniIRST-VL}, the first large-scale, highly diverse vision--language dataset for omni-domain IRST detection. It comprises over 39k annotations across six complementary instruction tasks covering both scene-level understanding and target-centric reasoning.

cs.CV↗

arXiv2026

Rethinking IRSTD: Single-Point Supervision Guided Encoder-only Framework is Enough for Infrared Small Target Detection

Infrared small target detection (IRSTD) aims to separate small targets from clutter backgrounds. Extensive research is dedicated to the pixel-level supervision-guided "encoder-decoder" segmentation paradigm. Although having achieved promising performance, they neglect the fact that small targets only occupy a few pixels and are usually accompanied with blurred boundary caused by clutter backgrounds. Based on this observation, we argue that the first principle of IRSTD should be target localization instead of separating all target region accompanied with indistinguishable background noise. In this paper, we reformulate IRSTD as a centroid regression task and propose a novel Single-Point Supervision guided Infrared Probabilistic Response Encoding method (namely, SPIRE), which is indeed challenging due to the mismatch between reduced supervision network and equivalent output. Specifically, we first design a Point-Response Prior Supervision (PRPS), which transforms single-point annotations into probabilistic response map consistent with infrared point-target response characteristics, with a High-Resolution Probabilistic Encoder (HRPE) that enables encoder-only, end-to-end regression without decoder reconstruction. By preserving high-resolution features and increasing effective supervision density, SPIRE alleviates optimization instability under sparse target distributions. Finally, extensive experiments on various IRSTD benchmarks, including SIRST-UAVB and SIRST4 demonstrate that SPIRE achieves competitive target-level detection performance with consistently low false alarm rate (Fa) and significantly reduced computational cost. Code is publicly available at: https://github.com/NIRIXIANG/SPIRE-IRSTD.

cs.CV↗

arXiv2026

CodeSpecBench: Benchmarking LLMs for Executable Behavioral Specification Generation

Large language models (LLMs) can generate code from natural language, but the extent to which they capture intended program behavior remains unclear. Executable behavioral specifications, defined via preconditions and postconditions, provide a concrete means to assess such understanding. However, existing work on specification generation is constrained in evaluation methodology, task settings, and specification expressiveness. We introduce CodeSpecBench, a benchmark for executable behavioral specification generation under an execution-based evaluation protocol. CodeSpecBench supports both function-level and repository-level tasks and encodes specifications as executable Python functions. Constructed from diverse real-world codebases, it enables a realistic assessment of both correctness (accepting valid behaviors) and completeness (rejecting invalid behaviors). Evaluating 15 state-of-the-art LLMs on CodeSpecBench, we observe a sharp performance degradation on repository-level tasks, where the best model attains only a 20.2% pass rate. We further find that specification generation is substantially more challenging than code generation, indicating that strong coding performance does not necessarily reflect deep understanding of intended program semantics. Our data and code are available at https://github.com/SparksofAGI/CodeSpecBench.

cs.SE↗

arXiv2026

Geometric Height on Flag Varieties in Positive Characteristic

Let $k$ be an algebraically closed field of characteristic $p\neq 0$. Let $G$ be a connected reductive group over $k$, $P \subseteq G$ be a parabolic subgroup and $λ: P \longrightarrow \mathbb G_m$ be a strictly anti-dominant character. Let $C$ be a projective smooth curve over $k$ with function field $K=k(C)$ and $F$ be a principal $G$-bundle on $C$. Then $F/P \longrightarrow C$ is a flag bundle and $\mathcal{L}_λ=F \times_P k_λ$ on $F/P$ is a relatively ample line bundle. We compute the height filtration and successive minima of the height function $h_{\mathcal{L}_λ}: X(\overline{K}) \longrightarrow \mathbb{R}$ over the flag variety $X=(F/P)_K$.

math.NT↗

arXiv2025

Planetary Nebulae in the eROSITA eRASS1 catalog

Some planetary nebulae (PNe) host X-ray-emitting hot bubbles shaped by stellar wind interactions and/or harbor X-ray-emitting central stars due to accretion, shocks within their fast stellar winds, or even chromospheric emission from binary companions. In both cases, the properties of the X-ray emission critically probe late-stages of stellar evolution for such low- and intermediate-mass stars. While extant Chandra and XMM-Newton observations have detected X-ray emission in PNe, the numbers known remain very small ($\sim40$) compared to the overall Galactic PNe population ($\sim4000$). We have initiated a project aimed at increasing the sample of known PNe with X-ray emission using both current and new space-based X-ray telescopes such as the Einstein probe. To further investigate their X-ray properties to elucidate what drives current X-ray PN detections, we have cross-searched the SRG {\it eROSITA-DE} eRASS1 source catalogue and Hong Kong (HASH) PNe Database. Five known X-ray PNe have been detected (Abell\,30, NGC\,2392, NGC\,3242, NGC\,5315, and LoTr\,5), two new X-ray PNe are revealed (IC\,1297 and NGC\,2867), one (K\,1-27) is removed from previous X-ray compilations, and another 11 previously detected X-ray emitting PNe are not recovered. A comparison of the X-ray flux of detected and undetected X-ray PNe reveals that eROSITA eRASS1 is sensitive to PNe with X-ray fluxes larger than $\approx2\times10^{-14}$ erg~cm$^{-2}$~s$^{-1}$. The frequency of occurrence is $\simeq$0.5\% among the 1430 HASH True PNe in the eRASS1 footprint.

astro-ph.HE↗

arXiv2025

FinChart-Bench: Benchmarking Financial Chart Comprehension in Vision-Language Models

Large vision-language models (LVLMs) have made significant progress in chart understanding. However, financial charts, characterized by complex temporal structures and domain-specific terminology, remain notably underexplored. We introduce FinChart-Bench, the first benchmark specifically focused on real-world financial charts. FinChart-Bench comprises 1,200 financial chart images collected from 2015 to 2024, each annotated with True/False (TF), Multiple Choice (MC), and Question Answering (QA) questions, totaling 7,016 questions. We conduct a comprehensive evaluation of 25 state-of-the-art LVLMs on FinChart-Bench. Our evaluation reveals critical insights: (1) the performance gap between open-source and closed-source models is narrowing, (2) performance degradation occurs in upgraded models within families, (3) many models struggle with instruction following, (4) both advanced models show significant limitations in spatial reasoning abilities, and (5) current LVLMs are not reliable enough to serve as automated evaluators. These findings highlight important limitations in current LVLM capabilities for financial chart understanding. The FinChart-Bench dataset is available at https://huggingface.co/datasets/Tizzzzy/FinChart-Bench.

cs.CV↗