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Jun Yang

Publications and source records attributed to Jun Yang.

At least 55 records · Page 3Linked to original sources
arXiv2026

Advancing Astrophysics with the SKA II

Advancing Astrophysics with the SKA II (AASKAII), written by our science community, outlines the transformative scientific advances that will be enabled by the SKA telescopes. In the decade since the publication of the previous edition, telescope designs have matured, construction has commenced, and the SKA Organisation has evolved into the SKA Observatory (SKAO). At the same time, observations from SKA precursor and pathfinder telescopes have provided new insights into longstanding scientific challenges while revealing entirely new phenomena. Published in advance of the first science verification campaign for the SKA Observatory, this volume looks ahead to the coming decades of discovery and innovation in radio astronomy. AASKAII spans the broad range of scientific research enabled by the SKA telescopes, SKA-Mid and SKA-Low. The contributions are organised into six thematic categories according to their scientific focus. The opening section presents overview chapters from the SKA Science Working Groups, around which our community is organised. Each overview provides the broader context that connects the contributions in this volume to the key scientific questions being pursued by their respective communities.

astro-ph.IM↗
arXiv2026

BaCon: Efficient Batch Processing of Counting Queries [Full Version]

Counting queries are ubiquitous in database systems, particularly for driving internal system optimization. Learned models for cardinality estimation rely heavily on large-scale training data, yet generating such data by executing massive batches of counting queries is expensive. We propose BaCon, an efficient algorithm for batch evaluation of counting queries on top of a database system, without modifying its internals. BaCon integrates the idea of factorized databases with a workload-aware domain quantization strategy, allowing it to evaluate batches of counting queries using compact data structures rather than materializing massive join results. BaCon's design is compatible with most database management system, and we have implemented it as a client-side application on PostgreSQL with a lightweight C-language UDF (user-defined function). This implementation delivers speedups between 2$\times$ and 178$\times$ over baselines and good performance across various workloads, making training and maintenance of learned cardinality estimation models significantly more practical.

cs.DB↗
arXiv2026

Beyond Monotonic Progress: Retry-Supervised Value Learning for Robot Imitation

Human demonstrations for robot imitation learning often contain mistakes and corrective behaviors, such as imprecise grasps, object misalignment, unstable contact, and repeated attempts. While these segments are commonly treated as noisy or suboptimal data, they provide valuable evidence about when execution deviates from a desirable path and how task feasibility can be restored. However, existing reward and value models often rely on monotonic progress assumptions, which capture coarse task advancement but may overlook local execution errors and corrective behaviors in imperfect demonstrations. In this work, we propose ReTVL (ReTry-Supervised Value Learning), a framework for learning mistake-sensitive value functions from mixed-quality robot demonstrations by leveraging retry events as sparse supervision. ReTVL captures the local degradation-and-recovery structure around mistakes by combining global progress calibration with local pairwise preference learning induced by sparsely annotated retry keypoints. The learned value model is then used to reweight demonstration chunks for downstream behavior cloning, reducing the influence of harmful execution errors while preserving useful corrective behaviors. Experiments on real-robot manipulation tasks show that ReTVL produces more fine-grained value estimates than progress-based baselines and improves imitation learning from imperfect demonstrations.

cs.RO↗
arXiv2026

UniSGR: Unified Framework for Semantic ID Generation and Ranking

Recommendation systems play a pivotal role in modern e-commerce platforms. While generative retrieval has emerged as a promising paradigm for alleviating the limitations of multi-stage cascade architectures, existing methods still struggle with fine-grained multi-objective ranking. To bridge this gap, we propose UniSGR, a Unified framework for Semantic ID Generation and Ranking. UniSGR adopts a two-stage training paradigm: a multi-scenario pre-training stage that learns from mixed business-scenario data, followed by a scenario-specific alignment stage that jointly optimizes Value-Aware Parallel Multi-Token Prediction (VA-PMTP) and a unified multi-objective ranking module. To better align generation with downstream ranking, we introduce Task-Aware Tokens (TAT) guided by Funnel-Aware Contrastive Learning. Furthermore, we propose Semantic Tree Attention with Reorganized KV cache (STARK), an inference strategy that removes key efficiency bottlenecks in conventional beam search. Extensive offline experiments on a large-scale e-commerce platform demonstrate the effectiveness and scalability of UniSGR.

cs.IR↗
arXiv2026

Estimating Velocity and Spin of Spherical Objects from Rolling-Shutter Image(s)

Rolling-shutter cameras introduce characteristic distortions when imaging fast moving objects, and these effects are typically treated as artifacts to be corrected. In this work, we instead leverage rolling-shutter distortions as a valuable source of temporal information to estimate the 3D translational and angular velocities of rapidly moving spherical objects from a single rolling-shutter frame. We design a robust and easily detectable spherical pattern and propose a correspondence-free formulation that recovers motion by enforcing geometric consistency in a back-projection framework. By exploiting the geometry of the sphere, translational and rotational motions are decoupled and estimated through a two-stage optimization process, enabling reliable velocity recovery even for textureless objects. Extensive experiments on both synthetic and real datasets demonstrate accurate and robust estimation of motion parameters under challenging high-speed conditions.

cs.CV↗
arXiv2026

Enhancing VLBI Capability with the SKA-Mid and the Jingdong 120-m Radio Telescope

The Jingdong Radio Telescope (JRT) is a 120-meter fully steerable radio telescope currently under construction in Jingdong County, Yunnan Province, China. Located at a relatively low latitude (24.5 degree), the JRT will enable observations of nearly 90% of the sky. Equipped with two broadband single-pixel receivers covering 1-8 GHz and 6-18 GHz, and a powerful digital backend, the telescope will support single-dish studies of various radio sources-particularly millisecond pulsars for enhancing the detection of nanohertz gravitational waves. In addition to single-dish capabilities, the JRT is expected to contribute approximately 800 hours annually to international Very Long Baseline Interferometry (VLBI) observations via a standard VLBI backend. When operating in conjunction with the phased-up SKA-Mid, the JRT will significantly enhance the technical and scientific capabilities of existing VLBI networks. This paper presents a comprehensive overview of the JRT's VLBI module and explores its potential to improve joint VLBI observations with current VLBI networks. Our analysis suggests that coordinated VLBI observations involving both the SKA-Mid and the JRT have the potential to significantly advance the field. For early sciences, we also highlight a few highly promising scientific cases, e.g. measuring the distance to PSR J0437-4715 with <1 ly accuracy and exploring jet formation with an event-horizon-scale resolution in M60*.

astro-ph.IM↗
arXiv2026

Mapping the Milky Way with Masers

SKA-VLBI is poised to revolutionize our understanding of the Galactic structure through its unprecedented astrometric precision and sensitivity. As a next-generation facility, it will answer long-standing questions about the Galactic structure by mapping its entire spiral structure in detail, spanning from the solar neighborhood, through the Galactic Center, to the far side of the Milky Way. Its access to the Southern sky will allow us to obtain more precise 3D parameters of the Galactic bar, reveal the nature of the 3-kpc Arm, and clarify the dynamical coupling between the bar and the spiral arms. By leveraging high-precision astrometry of numerous celestial objects with SKA-VLBI, the Galactic fundamental parameters such as the Solar motion and the Galactic rotation curve can be constrained more precisely. These advancements will not only elucidate the structure of our Milky Way, but also provide benchmarks for understanding barred spiral galaxies in general. Furthermore, they are important for advancing our knowledge of cosmological structure formation. The capabilities of SKA-VLBI will open a new era of high precision Galactic astrometry.

astro-ph.GA↗
arXiv2026

ProMSA:Progressive Multimodal Search Agents for Knowledge-Based Visual Question Answering

Knowledge-based Visual Question Answering (KB-VQA) requires models to combine image understanding with external knowledge. Most prior methods use a fixed retrieve-then-generate pipeline with a pre-selected retriever and a static top-k setting, which is not adaptive during reasoning. We propose ProMSA, a progressive multimodal search agent for KB-VQA. Given an image-question pair, the agent iteratively chooses image search, text search, or stop, under explicit tool-call budgets and with deduplication to avoid redundant retrieval. For training, we first use rejection-sampling SFT to learn valid tool-use formats, then optimize the agent with TN-GSPO, a sequence-level RL objective that normalizes updates by both generation length and tool-interaction depth. Experiments on E-VQA and InfoSeek show consistent gains over strong RAG and agent baselines, and improved retrieval and end-to-end accuracy. The code is available at https://github.com/DingWu1021/Promsa.

cs.CV↗
arXiv2026

Exploring Tidal Disruption Events with SKA and VLBI: Unveiling the Mystery of Black Hole Feeding and Outflows

Tidal disruption events (TDEs) probe the birth and evolution of black hole accretion flows and jets on human timescales. Radio emission traces shocks and outflows from thermal TDEs and powerful relativistic jets in the rare jetted class. SKA Mid, phased for VLBI and used together with global networks, will deliver milliarcsecond imaging, tens of microarcsecond astrometry, and microJy sensitivity, enabling: (i) proper motion measurements that discriminate off axis relativistic jets from subrelativistic winds; (ii) resolved morphologies and magnetic field diagnostics via polarimetry; and (iii) precise nuclear localization to distinguish SMBH vs. IMBH and to reveal recoiling or binary systems. SKA's wide frequency coverage (0.35 to 15.4 GHz) and 1h continuum sensitivities of 3 to 10 microJy per beam, together with multibeam tiedarray VLBI and a transient buffer for rapid triggers, are transformational. LSST, Einstein Probe, and SVOM will increase TDE alerts to hundreds per year, and late time radio flares appear common, ensuring rich SKA VLBI samples. We provide observing strategies, detection forecasts, and predictions, e.g., about 5 proper motion detections of jetted (or off axis) TDEs per year and routine core shift constraints at the microarcsecond level. This program will establish TDEs as laboratories for exploring jet launching, particle acceleration (including neutrinos), black hole accretion history and demographics, and properties of circumnuclear medium.

astro-ph.HE↗
arXiv2026

AGN Jets from Formation to Dissipation

Active Galactic Nuclei (AGN) are among the most energetic phenomena in the Universe, capable of launching powerful relativistic jets that extend from sub-parsec to megaparsec scales. These jets play a crucial role in regulating star formation, redistributing energy and matter, and shaping the evolution of galaxies and their environments. Despite decades of study, a comprehensive understanding of how AGN jets form, propagate, and dissipate remains elusive. The aim of this chapter is to highlight how the future capabilities of the the Square Kilometre Array (SKA), as a standalone array as well as in combination with Very Long Baseline Interferometry (VLBI) arrays and multi-wavelength facilities, will transform our capabilities to study the co-evolution of AGN jets and their host galaxies from jet formation to dissipation scales.

astro-ph.GA↗
arXiv2026

Evaluating Learned Spatial Indexes

Learned indexes improve query performance by adapting search structures to data and workload distributions. Although many learned indexes have been proposed, their trade-offs remain insufficiently understood for spatial range queries, where performance depends not only on model accuracy but also on data and query skew, layout granularity, selectivity, and storage behavior. In this work, we perform an experimental study of learned indexes for spatial range queries. We examine a representative set of indexes and address seven fundamental questions: (1) How does block size influence query latency, and what configurations yield optimal performance under varying selectivities? (2) How do skewed data and query distributions impact index performance? (3) How do indexes balance refinement and scan costs, and which designs favor one over the other? (4) How do disk-based storage conditions alter optimal block size and latency trade-offs compared to in-memory settings? (5) What are the construction costs of different indexes, and under what query volumes are these costs amortized? (6) For a given data and query workload, which index is expected to perform best? (7) Do index-selection insights learned from synthetic data generalize to real-world data distributions? To enable the analysis, we use a framework with a common storage backend, standardized query execution pipelines, and controlled variations in data and query skew. Our experiments reveal critical insights into refinement vs. scan trade-offs, the impact of block size, and the interplay between selectivity and layout effectiveness. We synthesize these findings into a workload-based decision tree for index selection and validate it on real OpenStreetMap point sets with synthetic queries, confirming that its recommendations exhibit minimal decision regret and typically yield near-optimal query performance.

cs.DB↗
arXiv2026

SEAGym: An Evaluation Environment for Self-Evolving LLM Agents

Self-evolving LLM-based agents improve mainly by changing their agent harness: the structured execution layer around a base model, including prompts, memory, tools, middleware, runtime state, and the model-tool interaction loop. Existing evaluations often reduce this process to isolated task scores or a single sequential curve, obscuring whether an update produces reusable improvement, overfits recent tasks, increases cost, or harms older behavior. We introduce SEAGym, an evaluation environment for measuring agent harness updates across training, validation, test, replay, and cost records. SEAGym turns Harbor-compatible benchmarks into dynamic self-evolution task sources with train batches, frozen update-validation, held-out ID and OOD transfer views, replay diagnostics, and saved snapshot and metric records. Instantiating SEAGym on Terminal-Bench 2.0 and HLE, we compare ACE, TF-GRPO, and AHE under a shared epoch/batch protocol. The results show that these evaluation views provide complementary signals about the evolution process: frequent updates may fail to improve held-out performance, useful intermediate snapshots may collapse later, and source diversity and model backend can affect harness reliability.

cs.AI↗
arXiv2026

A Jet from a Nearly Dormant Black Hole

Most galaxies host supermassive black holes (SMBHs) that remain weakly accreting or dormant for much of their lifetimes. At the lowest accretion rates, these systems may represent the transition between active nuclei and dormant black holes, but whether they can still launch collimated jets remains unclear. The nuclei in our Galaxy (\sgra) and M31 are key examples of this regime, although no clear jet structure has yet been detected in either source. Here we report multi-frequency very long baseline interferometric observations of \Msixty\ (NGC~4649), a nearby elliptical galaxy hosting a nearly dormant SMBH with an Eddington ratio of $\sim10^{-8}$. We detect a compact two-sided jet with an unusually steep synchrotron spectrum, demonstrating that collimated outflows can persist even under nearly dormant accretion conditions. The apparent radio core exhibits an unprecedentedly steep frequency-dependent position shift toward the SMBH, locating the central engine only $\sim57\,μ$as, corresponding to a projected distance of $\sim10$ Schwarzschild radii, upstream of the 8.37-GHz core. The observed jet morphology and steep core-shift behaviour are reproduced by general relativistic magnetohydrodynamic and radiative-transfer simulations, indicating a magnetically dominated, non-equipartition jet-launching region that departs from the standard conical equipartition picture. These results provide direct observational evidence that jet production can survive near the dormant SMBHs and establish \Msixty\ as a unique laboratory for probing jet formation on event-horizon scales in the lowest-accretion SMBH regime.

astro-ph.HE↗
arXiv2026

Detection of a parsec-scale, compact, and fading ejecta from an accreting massive black hole

Dwarf galaxies, characterized by their low luminosities and masses, are excellent candidates for searches for intermediate-mass black holes (IMBHs), particularly when they show strong accretion and ejection activity. The dwarf galaxy SDSS J101747.09+393207.7 has recently been found to display a very high X-ray luminosity and an X-shaped optical structure, possibly caused by a dwarf--dwarf merger. To explore its potential IMBH ejection activity, we performed very long baseline interferometry (VLBI) observations at 4.9 GHz. In this work, we present the detection of a milliarcsecond-scale, compact, sub-microjansky radio component near the optical centroid. According to some existing radio sky survey data, the radio component was not detected until 2015; it displayed an optically thin steep radio spectrum and declining flux densities across 0.8--5 GHz from 2019 to 2025. Therefore, we identify it as a short-lived and rarely seen ejecta that was produced by unstable accretion onto a massive black hole and likely faded away in a few decades. These results indicate that short-lived, episodic jet activity from accreting IMBHs in dwarf galaxies might exist.

astro-ph.HE↗
arXiv2026

Multi-GPU MBE(3)-OSV-MP2 for Performant Large-Scale ab initio Calculations

The computational acceleration of orbital-invariant local correlation methods on graphics processing units (GPUs) has remained largely unexplored due to substantial algorithmic complexities. The runtime efficiency of GPU-implemented local correlation theories can be significantly constrained by the parallelizable degree of the orbital localization procedure, the iterative solution of the local wave function, and the adaptation of CUDA kernels to inherently local or sparse operations. Using the second-order Møller-Plesset perturbation (MP2) theory, we present a multi-GPU implementation for large-scale third-order many-body expansion orbital-specific virtual MP2 (MBE(3)-OSV-MP2) energy calculations. Accordingly, our algorithms and implementation address the GPU parallelization ability for peak utilization and parallelism of local MP2 computation in several aspects, including Jacobi-Pipek-Mezey localization, randomized OSV generation, direct MP2 integral regeneration, as well as CUDA kernel adaptation to local operations. The GPU-based MBE(3)-OSV-MP2 energy computation achieves $O(N^{1.9})$ scaling and 84\% parallel efficiency up to 24 GPUs distributed on multiple nodes. The present implementation delivers 40-fold wall-time speedup of the canonical RI-MP2 and 10-fold speedup of the CPU-based MBE(3)-OSV-MP2 for (H$_2$O)$_{128}$/cc-pVDZ and (H$_2$O)$_{190}$/cc-pVDZ, respectively. A large scale computation of 784-atom insulin peptide yields the full MBE(3)-OSV-MP2 energies in 24 minutes with cc-pVDZ (7571 basis functions) and 6.4 hours with cc-pVTZ (17448 basis functions) on 8 NVIDIA A800 GPUs. Our work opens up new possibilities for performing fast GPU-based local correlation calculations on real-life macromolecules.

physics.chem-ph↗
arXiv2026

A 4.5-s Quasiperiodic Spectral Oscillation in GRB 230307A: Evidence for Free Precession of a Post-Merger Magnetar?

Millisecond magnetars, rapidly rotating neutron stars with ultra-strong magnetic fields, have long been proposed as central engines of gamma-ray bursts (GRBs). For GRBs produced by neutron star mergers, the survival of a long-lived magnetar remnant remains uncertain, as the merger remnant may rapidly collapse into a black hole. In GRB 230307A, multiwavelength observations together with a previously reported 909-Hz periodic signal consistent with millisecond spin in its prompt emission provide strong evidence that such a post-merger magnetar may power the burst. Here we report the discovery of a quasiperiodic modulation with a characteristic period of 4.5 s in the spectral evolution of GRB 230307A, detected consistently across multiple gamma-ray instruments. The modulation is manifested as a coherent, energy-dependent variation of the spectral shape, with the strongest signature in the evolution of the peak energy. Within the magnetar-engine framework, such a low-frequency modulation can be interpreted as a manifestation of large-scale periodic variations associated with the central engine. If interpreted in terms of free precession, the observed timescale implies a stellar ellipticity of $ε\gtrsim 2.4 \times 10^{-4}$, corresponding to an internal magnetic field strength of $B_t \gtrsim 1.6 \times 10^{16}$ G, alongside a dipole field of $B_p \approx 5.6 \times 10^{15}$ G inferred from the early X-ray emission. These results suggest that such systems may provide potential sources of post-merger gravitational waves (GWs), motivating targeted searches following GRB triggers.

astro-ph.HE↗
arXiv2026

Jet-Structure Imprint on the Curvature Tail of Gamma-Ray Burst Prompt Emission

Even though the prompt emission of gamma-ray bursts (GRBs) is highly beamed, high-latitude emission still produces a distinct light curve break after the intrinsic emission ceases and the edge of the jet comes into view. This curvature effect offers a direct probe of the jet structure during the prompt phase. To uncover the geometric structure of the GRB jet encoded in the prompt light-curve evolution, we develop a numerical model that calculates synchrotron light curves from structured jets to interpret the observed break. We apply this model to the prompt emission of GRB 230307A, which displays a rare late-time break. Our analysis demonstrates that simple spherical outflow and top-hat jet models are inadequate to reproduce the light curve. Instead, the observations are best described by a power-law wing jet with a uniform core ($θ_{\rm core}=0.0147$ rad) and a surrounding power-law wing. Our results demonstrate that the break in late-time prompt emission can be a powerful diagnostic of GRB jet structure.

astro-ph.HE↗
arXiv2026

MAAS-SFRThelper: An Integrated ESAPI Plugin for Structure Generation, Optimization, and Evaluation of Spatially Fractionated Radiation Therapy

Spatially fractionated radiation therapy (SFRT) planning requires three coordinated tasks: generation of high-dose sphere structures, position-aware optimization, and peak-valley dose ratio evaluation. We present MAAS-SFRThelper, a shared-source Eclipse Scripting Application Programming Interface (ESAPI) plugin that integrates structure generation, geometric-aware optimization, and peak-valley dose ratio evaluation for SFRT into a single workflow inside Varian's Eclipse treatment planning system. The plugin exposes five task-oriented tabs sharing common services for sphere extraction and objective creation. The SphereLattice tab generates sphere lattices using five placement patterns. The Optimization tab searches over candidate lattice positions using a four-metric geometric surrogate score and triggers VMAT optimization and dose calculation. The Evaluation tab implements four analysis modes; its three-dimensional peak-valley classification recovers sphere centers from the lattice structure through a geometric extraction pipeline rather than relying on dose thresholds. We validated all functionality on digital phantoms against analytic ground truth. The plugin is distributed as source code under the Varian Limited Use Software License Agreement. Source code and documentation are publicly available on GitHub.

physics.med-ph↗