arXiv subjects

Xueyi Li

Publications and source records attributed to Xueyi Li.

7 recordsLinked to original sources

arXiv2026

ALMA CO(2-1) Gas Dynamics in NGC 315: A Multi-Method Benchmark for Supermassive Black Hole Mass Measurement

We present ALMA Cycle~7 \cotwo\ observations of the circumnuclear disk in NGC~315 at an angular resolution of $0\farcs230\times0\farcs175$, improving on past measurements and resolving the sphere of influence (SOI) of the supermassive black hole (SMBH), whose mass has previously been estimated of $M_{\rm BH}= \left(2.08^{+0.33}_{-0.15}\right) \times 10^9$~M$_\odot$ The high spatial resolution and sensitivity enable robust full-cube forward modeling of the molecular gas kinematics and a direct comparison of multiple independent gas-based dynamical modeling techniques. We apply standard Bayesian codes using both MCMC and nested sampling approaches, as well as a frequentist code to the same dataset, exploring systematic uncertainties associated with the stellar mass distribution, gas surface-brightness parameterization, and disk geometry. All methods yield consistent black hole masses, indicating that the inferred $M_{\rm BH}$ is not strongly method-dependent. Combining the ensemble of independent molecular-gas-based models, we derive an ensemble median black hole mass of $M_{\rm BH}/10^9\,\mathrm{M_\odot} = 2.02^{+0.04}_{-0.05}$(stat)$^{+0.05}_{-0.04}$(sys), where the comparable contributions to the full error budget arise from modeling systematics rather than formal fitting uncertainties. Our $M_{\rm BH}$ is consistent with the empirical $M_{\rm BH}$--$\sigma_\star$ and $M_{\rm BH}$--$L_{\rm bulge}$ scaling relations, and lies 32\% below an independent stellar-dynamical measurement, a discrepancy we discuss in the context of systematic differences between gas- and stellar-based methods. NGC~315 serves as a benchmark for quantifying molecular gas-dynamical $M_{\rm BH}$ systematic uncertainties and for future cross-comparisons of gaseous and stellar dynamical approaches.

astro-ph.GA↗

arXiv2026

GradingAttack: Exposing Security Vulnerabilities in LLM Based Educational Grading Agents

Large language models (LLMs) are increasingly deployed as educational agents for automatic short answer grading (ASAG) in real-world educational environments, significantly boosting assessment efficiency and scalability. However, when these grading agents operate ``in the wild'', their vulnerability to adversarial manipulation raises critical concerns about agent security and trustworthiness. In this paper, we introduce GradingAttack, a fine-grained adversarial attack framework that systematically evaluates the security vulnerabilities of LLM based educational grading agents. Specifically, we design token-level and prompt-level attack strategies that manipulate agent grading outcomes while maintaining high stealth, exposing fundamental weaknesses in current agent deployments. Experiments on multiple datasets demonstrate that both attack strategies effectively compromise grading agents, with prompt-level attacks achieving higher success rates and token-level attacks exhibiting superior stealth capability. Our findings reveal that current LLM based educational agents lack robust defenses against adversarial attacks, underscoring the urgent need for developing secure and trustworthy agent systems for critical educational applications.

cs.CR↗

arXiv2025

From Text to Talk: Audio-Language Model Needs Non-Autoregressive Joint Training

Recent advances in large language models (LLMs) have attracted significant interest in extending their capabilities to multimodal scenarios, particularly for speech-to-speech conversational systems. However, existing multimodal models handling interleaved audio and text rely on autoregressive (AR) methods, overlooking that text depends on target-target relations whereas audio depends mainly on source-target relations. In this work, we propose Text-to-Talk (TtT), a unified audio-text framework that integrates AR text generation with non-autoregressive (NAR) audio diffusion in a single Transformer. By leveraging the any-order AR property of absorbing discrete diffusion, our approach provides a unified training objective for text and audio. To support this hybrid generation paradigm, we design a modality-aware attention mechanism that enforces causal decoding for text while allowing bidirectional modeling within audio spans, and further introduce three training strategies that reduce train-test discrepancies. During inference, TtT employs block-wise diffusion to synthesize audio in parallel while flexibly handling variable-length outputs. Comprehensive experiments on Audio-QA, ASR, AAC and speech-to-speech benchmarks show that TtT consistently surpasses strong AR and NAR baselines, with additional ablation and training-strategy analyses confirming the contribution of each component. We will open-source our models, data and code to facilitate future research in this direction.

cs.CL↗

arXiv2025

CO Emission and Absorption-line Survey of the M87 Nucleus Using Archival ALMA Imaging

We present an M87 molecular line search from archival Atacama Large Millimeter/sub-millimeter Array (ALMA) imaging, covering the circumnuclear disk (CND) as well as ionized gas filaments and dusty cloud regions. We find no evidence for CO emission in the central $\sim$kpc and place an upper limit of $M_\mathrm{H_2} < 2.3\times 10^5$ $M_\odot$ in the atomic gas CND region, a factor of 20$\times$ lower than previous surveys. During this search, we discovered extragalactic CO absorption lines in the $J$ = 1$-$0, 2$-$1, and 3$-$2 transitions against the bright (Jy-scale) active nucleus. These CO lines are narrow ($\sim$5 km s$^{-1}$) and blueshifted with respect to the galaxy's systemic velocity by $-$75 to $-$84 \kms. This CO absorber appears to be kinematically distinct from outflowing atomic gas seen in absorption. Low integrated opacities ranging from $\tau_\mathrm{CO} \sim 0.02-0.06$ \kms\ and a column density $N_\mathrm{CO} \approx (1.2\pm0.2)\times 10^{15}$ cm$^{-2}$ translate to $N_\mathrm{H_2} \sim (1-2) \times 10^{20}$ cm$^{-2}$. CO excitation temperatures spanning $T_\mathrm{ex} \sim 8$ K to $\sim$30 K do not follow local thermodynamic equilibrium (LTE) expectations, and non-LTE radex radiative transfer modeling of the CO absorber is consistent with a number density $n_\mathrm{H_2} \sim 5000$ cm$^{-3}$ embedded in a $\sim$60 K environment. Taken together, the observed CO absorption lines are most consistent with a thin, pressure-confined filament seen slightly off-center from the M87 nucleus. We also explore the impact of residual telluric lines and atmospheric variability on narrow extragalactic line identification and demonstrate how bandpass calibration limitations may introduce broad but very low S/N and spurious absorption and emission signatures.

astro-ph.GA↗

arXiv2021

Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation

This paper addresses the task of unsupervised video multi-object segmentation. Current approaches follow a two-stage paradigm: 1) detect object proposals using pre-trained Mask R-CNN, and 2) conduct generic feature matching for temporal association using re-identification techniques. However, the generic features, widely used in both stages, are not reliable for characterizing unseen objects, leading to poor generalization. To address this, we introduce a novel approach for more accurate and efficient spatio-temporal segmentation. In particular, to address \textbf{instance discrimination}, we propose to combine foreground region estimation and instance grouping together in one network, and additionally introduce temporal guidance for segmenting each frame, enabling more accurate object discovery. For \textbf{temporal association}, we complement current video object segmentation architectures with a discriminative appearance model, capable of capturing more fine-grained target-specific information. Given object proposals from the instance discrimination network, three essential strategies are adopted to achieve accurate segmentation: 1) target-specific tracking using a memory-augmented appearance model; 2) target-agnostic verification to trace possible tracklets for the proposal; 3) adaptive memory updating using the verified segments. We evaluate the proposed approach on DAVIS$_{17}$ and YouTube-VIS, and the results demonstrate that it outperforms state-of-the-art methods both in segmentation accuracy and inference speed.

cs.CV↗

arXiv2020

Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation

Acquiring sufficient ground-truth supervision to train deep visual models has been a bottleneck over the years due to the data-hungry nature of deep learning. This is exacerbated in some structured prediction tasks, such as semantic segmentation, which requires pixel-level annotations. This work addresses weakly supervised semantic segmentation (WSSS), with the goal of bridging the gap between image-level annotations and pixel-level segmentation. We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pseudo ground-truths, which can be used for training more accurate segmentation models. In particular, we devise a graph neural network (GNN) for group-wise semantic mining, wherein input images are represented as graph nodes, and the underlying relations between a pair of images are characterized by an efficient co-attention mechanism. Moreover, in order to prevent the model from paying excessive attention to common semantics only, we further propose a graph dropout layer, encouraging the model to learn more accurate and complete object responses. The whole network is end-to-end trainable by iterative message passing, which propagates interaction cues over the images to progressively improve the performance. We conduct experiments on the popular PASCAL VOC 2012 and COCO benchmarks, and our model yields state-of-the-art performance. Our code is available at: https://github.com/Lixy1997/Group-WSSS.

cs.CV↗

arXiv2017

Wireless Powered Cooperative Jamming for Secure OFDM System

This paper studies the secrecy communication in an orthogonal frequency division multiplexing (OFDM) system, where a source sends confidential information to a destination in the presence of a potential eavesdropper. We employ wireless powered cooperative jamming to improve the secrecy rate of this system with the assistance of a cooperative jammer, which works in the harvest-then-jam protocol over two time-slots. In the first slot, the source sends dedicated energy signals to power the jammer; in the second slot, the jammer uses the harvested energy to jam the eavesdropper, in order to protect the simultaneous secrecy communication from the source to the destination. In particular, we consider two types of receivers at the destination, namely Type-I and Type-II receivers, which do not have and have the capability of canceling the (a-priori known) jamming signals, respectively. For both types of receivers, we maximize the secrecy rate at the destination by jointly optimizing the transmit power allocation at the source and the jammer over sub-carriers, as well as the time allocation between the two time-slots. First, we present the globally optimal solution to this problem via the Lagrange dual method, which, however, is of high implementation complexity. Next, to balance tradeoff between the algorithm complexity and performance, we propose alternative low-complexity solutions based on minorization maximization and heuristic successive optimization, respectively. Simulation results show that the proposed approaches significantly improve the secrecy rate, as compared to benchmark schemes without joint power and time allocation.

cs.IT↗