arXiv subjects

Yiyang Lin

Publications and source records attributed to Yiyang Lin.

15 recordsLinked to original sources

arXiv2026

LightNav-0: Eliciting VLM Spatial Intelligence for Generalist Embodied Navigation

Embodied navigation requires agents to translate heterogeneous goals and visual observations into actions across tasks, environments, and robot embodiments. Modern vision-language models (VLMs) already encode spatial priors for visual grounding, spatial reasoning, and pointing, but these capabilities are rarely elicited directly for robot control. Existing navigation systems instead rely on task- or embodiment-specific components, fragmenting perception, reasoning, and action while offering limited generalization. Here we present LightNav-0, a compact generalist embodied navigation model that elicits the spatial intelligence of a pretrained VLM and aligns it with navigation, without task-specific prediction heads. LightNav-0 represents diverse navigation tasks through a unified token interface: dual-channel pointing expresses task-, scene-, and embodiment-agnostic spatial intent, while a residual vector-quantized action tokenizer maps this intent to precise, embodiment-specific trajectories. Together with temporally aware visual history compression, ER mid-training, supervised fine-tuning, and reinforcement learning, this formulation supports instruction following, open-vocabulary object navigation, and visual tracking within a single model. The navigation training corpus spans 2K+ scenes and 4K+ hours of embodied navigation data. LightNav-ER, the embodied-reasoning checkpoint used to initialize LightNav-0, attains the highest complete-set average across 8 embodied-reasoning benchmarks, while LightNav-0 achieves state-of-the-art monocular success rates across all 10 public navigation simulation settings. Real-world evaluations further demonstrate zero-shot generalization across robot embodiments, diverse scenes, and static and dynamic targets. These results establish compact VLMs as a unified and transferable backbone for generalist embodied navigation.

cs.RO↗

arXiv2026

Bolometric correction factor and radiative efficiency for the super-Eddington accretion flow in tidal disruption events

The estimate of the bolometric luminosity and the radiative efficiency are two key aspects for understanding the properties of the accretion flow around a supermassive black hole (BH). In this paper, we focus on the estimate of the bolometric luminosity and the radiative efficiency of the early super-Eddington accretion flow in tidal disruption events (TDEs). Specifically, we first perform radiation hydrodynamic simulations of super-Eddington accretion flow in TDE environment, and then calculate the corresponding emergent spectra with the method of post processing for the simulation data. Based on the emergent spectra, we calculate the isotropic-equivalent X-ray bolometric correction factor $k_\mathrm{bol}$ and the radiative efficiency $\eta$ of the super-Eddington accretion flow. We find that both $k_\mathrm{bol}$ and $\eta$ are BH mass and viewing-angle dependent. $k_\mathrm{bol}$ is in the range of about a few tens to a few thousands, and $\eta$ is in the range of $\sim 10^{-3}-10^{-1}$ for BH mass in the range of $10^{6-7}M_\odot$ and the viewing angle in the range of $0^{\rm o}-90^{\rm o}$. Finally, we apply the derived $k_\mathrm{bol}$ and $\eta$ to some specific TDEs to estimate the accreted mass during an event, which can significantly alleviate the so-called missing energy problem in TDEs.

astro-ph.HE↗

arXiv2026

Radiation hydrodynamic simulations for the origin of quasi-periodic oscillations for accretion onto supermassive black holes

Quasi-periodic oscillation (QPO) has been detected in several accreting supermassive black hole (SMBH) systems, including active galactic nuclei (AGNs) and tidal disruption events (TDEs). However, despite that several models have been proposed, the physical origin of QPO is still unclear. In this paper, we performed radiation hydrodynamic simulations of accretion flow by injecting mass at a fixed radius, i.e. 10 Schwarzschild radius with different mass accretion rates, and setting the black hole (BH) mass to $10^7M_{\odot}$. We find that there are QPO signals by analyzing the mass inflow rates as a function of time from the simulations for different radii. The QPO frequencies from our simulations are well consistent with the radial epicyclic frequencies from analytic calculations for radius greater than a critical radius 3.8 Schwarzschild radius. This critical radius corresponds to the maximum epicyclic frequency, i.e. $\nu_{\rm r,max}$, in the radial direction. We proposed that $\nu_{\rm r,max}$ can be a good proxy for the observed QPO $\nu_{\rm QPO}$. Furthermore, assuming that our simulation results can be scaled to different BH masses $M_{\rm BH}$, we find that the theoretical relation of $\nu_{\rm r,max}$ as a function of $M_{\rm BH}$ can well match $\nu_{\rm QPO}$ as a function of $M_{\rm BH}$ for a sample of AGN and TDE. Finally, we discuss the effects of the BH mass, general relativity (GR), and other possible factors including the size of the mass injecting radius, viscosity and magnetic field on the simulation results.

astro-ph.HE↗

arXiv2026

TAIHRI: Task-Aware 3D Human Keypoints Localization for Close-Range Human-Robot Interaction

Accurate 3D human keypoints localization is a critical technology enabling robots to achieve natural and safe physical interaction with users. Conventional 3D human keypoints estimation methods primarily focus on the whole-body reconstruction quality relative to the root joint. However, in practical human-robot interaction (HRI) scenarios, robots are more concerned with the precise metric-scale spatial localization of task-relevant body parts under the egocentric camera 3D coordinate. We propose TAIHRI, the first Vision-Language Model (VLM) tailored for close-range HRI perception, capable of understanding users' motion commands and directing the robot's attention to the most task-relevant keypoints. By quantizing 3D keypoints into a finite interaction space, TAIHRI precisely localize the 3D spatial coordinates of critical body parts by 2D keypoint reasoning via next token prediction, and seamlessly adapt to downstream tasks such as natural language control or global space human mesh recovery. Experiments on egocentric interaction benchmarks demonstrate that TAIHRI achieves superior estimation accuracy for task-critical body parts. We believe TAIHRI opens new research avenues in the field of embodied human-robot interaction. Code is available at: https://github.com/Tencent/TAIHRI.

cs.CV↗

arXiv2026

UniField: A Unified Field-Aware MRI Enhancement Framework

Magnetic Resonance Imaging (MRI) field-strength enhancement holds immense value for both clinical diagnostics and advanced research. However, existing methods typically focus on isolated enhancement tasks, such as specific 64mT-to-3T or 3T-to-7T transitions using limited subject cohorts, thereby failing to exploit the shared degradation patterns inherent across different field strengths and severely restricting model generalization. To address this challenge, we propose \methodname, a unified framework integrating multiple modalities and enhancement tasks to mutually promote representation learning by exploiting these shared degradation characteristics. Specifically, our main contributions are threefold. Firstly, to overcome MRI data scarcity and capture continuous anatomical structures, \methodname departs from conventional methods that treat 3D MRI volumes as independent 2D slices. Instead, we directly exploit comprehensive 3D volumetric information by leveraging pre-trained 3D foundation models, thereby embedding generalized and robust structural representations to significantly boost enhancement performance. In addition, to mitigate the spectral bias of mainstream flow-matching models that often over-smooth high-frequency details, we explicitly incorporate the physical mechanisms of magnetic fields to introduce a Field-Aware Spectral Rectification Mechanism (FASRM), tailoring customized spectral corrections to distinct field strengths. Finally, to resolve the fundamental data bottleneck, we organize and publicly release a comprehensive paired multi-field MRI dataset, which is an order of magnitude larger than existing datasets. Extensive experiments demonstrate our method's superiority over state-of-the-art approaches, achieving an average improvement of approximately 1.81 dB in PSNR and 9.47% in SSIM. Codes and datasets are available at: https://github.com/linyiyang98/UniField.

cs.CV↗

arXiv2025

AeroThrow: An Autonomous Aerial Throwing System for Precise Payload Delivery

Autonomous aerial systems play an increasingly vital role in a wide range of applications, particularly for transport and delivery tasks in complex environments. In airdrop missions, these platforms face the dual challenges of abrupt control mode switching and inherent system delays along with control errors. To address these issues, this paper presents an autonomous airdrop system based on an aerial manipulator (AM). The introduction of additional actuated degrees of freedom enables active compensation for UAV tracking errors. By imposing smooth and continuous constraints on the parabolic landing point, the proposed approach generates aerial throwing trajectories that are less sensitive to the timing of payload release. A hierarchical disturbance compensation strategy is incorporated into the Nonlinear Model Predictive Control (NMPC) framework to mitigate the effects of sudden changes in system parameters, while the predictive capabilities of NMPC are further exploited to improve the precision of aerial throwing. Both simulation and real-world experimental results demonstrate that the proposed system achieves greater agility and precision in airdrop missions.

cs.RO↗

arXiv2025

IRS: Instance-Level 3D Scene Graphs via Room Prior Guided LiDAR-Camera Fusion

Indoor scene understanding remains a fundamental challenge in robotics, with direct implications for downstream tasks such as navigation and manipulation. Traditional approaches often rely on closed-set recognition or loop closure, limiting their adaptability in open-world environments. With the advent of visual foundation models (VFMs), open-vocabulary recognition and natural language querying have become feasible, unlocking new possibilities for 3D scene graph construction. In this paper, we propose a robust and efficient framework for instance-level 3D scene graph construction via LiDAR-camera fusion. Leveraging LiDAR's wide field of view (FOV) and long-range sensing capabilities, we rapidly acquire room-level geometric priors. Multi-level VFMs are employed to improve the accuracy and consistency of semantic extraction. During instance fusion, room-based segmentation enables parallel processing, while the integration of geometric and semantic cues significantly enhances fusion accuracy and robustness. Compared to state-of-the-art methods, our approach achieves up to an order-of-magnitude improvement in construction speed while maintaining high semantic precision. Extensive experiments in both simulated and real-world environments validate the effectiveness of our approach. We further demonstrate its practical value through a language-guided semantic navigation task, highlighting its potential for real-world robotic applications.

cs.RO↗

arXiv2025

Early evolution of super-Eddington accretion flow in tidal disruption events

Tidal disruption events (TDEs) are luminous black hole (BH) transient sources, which are detected mainly in X-ray and optical bands. It is generally believed that the X-ray emission in TDEs is produced by an accretion disc formed as the stellar debris accreted onto the central BH. The origin of the optical emission is not determined, but could be explained by the `reprocessing' model with the X-ray emission reprocessed into optical band by a surrounding optically thick envelope or outflow. In this paper, we performed radiation hydrodynamic simulations of super-Eddington accretion flow with Athena++ code in the environment of TDEs, i.e., injecting a continuous mass flow rate at the circularization radius in the form of $\dot M_{\rm inject} \propto t^{-5/3}$ for the mass supply rate. We show that a significant fraction of the matter in the accretion inflow are blowed off forming outflow, and the properties of the outflow are viewing-angle dependent. We further calculate the emergent spectra of such an inflow/outflow system for different viewing angles with the method of Monto Carlo radiative transfer. Based on the emergent spectra, we show that the observed features of TDEs, such as the X-ray and optical luminosities, the blackbody temperature of X-ray and optical emission and the corresponding emission radii, the ratio of X-ray luminosity to optical luminosity, as well as the evolution of these quantities can be explained in the framework of viewing-angle effect of super-Eddington accretion around a BH.

astro-ph.HE↗

arXiv2025

CREATE-FFPE: Cross-Resolution Compensated and Multi-Frequency Enhanced FS-to-FFPE Stain Transfer for Intraoperative IHC Images

In the immunohistochemical (IHC) analysis during surgery, frozen-section (FS) images are used to determine the benignity or malignancy of the tumor. However, FS image faces problems such as image contamination and poor nuclear detail, which may disturb the pathologist's diagnosis. In contrast, formalin-fixed and paraffin-embedded (FFPE) image has a higher staining quality, but it requires quite a long time to prepare and thus is not feasible during surgery. To help pathologists observe IHC images with high quality in surgery, this paper proposes a Cross-REsolution compensATed and multi-frequency Enhanced FS-to-FFPE (CREATE-FFPE) stain transfer framework, which is the first FS-to-FFPE method for the intraoperative IHC images. To solve the slide contamination and poor nuclear detail mentioned above, we propose the cross-resolution compensation module (CRCM) and the wavelet detail guidance module (WDGM). Specifically, CRCM compensates for information loss due to contamination by providing more tissue information across multiple resolutions, while WDGM produces the desirable details in a wavelet way, and the details can be used to guide the stain transfer to be more precise. Experiments show our method can beat all the competing methods on our dataset. In addition, the FID has decreased by 44.4%, and KID*100 has decreased by 71.2% by adding the proposed CRCM and WDGM in ablation studies, and the performance of a downstream microsatellite instability prediction task with public dataset can be greatly improved by performing our FS-to-FFPE stain transfer.

cs.CV↗

arXiv2025

ConcealGS: Concealing Invisible Copyright Information in 3D Gaussian Splatting

With the rapid development of 3D reconstruction technology, the widespread distribution of 3D data has become a future trend. While traditional visual data (such as images and videos) and NeRF-based formats already have mature techniques for copyright protection, steganographic techniques for the emerging 3D Gaussian Splatting (3D-GS) format have yet to be fully explored. To address this, we propose ConcealGS, an innovative method for embedding implicit information into 3D-GS. By introducing the knowledge distillation and gradient optimization strategy based on 3D-GS, ConcealGS overcomes the limitations of NeRF-based models and enhances the robustness of implicit information and the quality of 3D reconstruction. We evaluate ConcealGS in various potential application scenarios, and experimental results have demonstrated that ConcealGS not only successfully recovers implicit information but also has almost no impact on rendering quality, providing a new approach for embedding invisible and recoverable information into 3D models in the future.

cs.CV↗

arXiv2024

AV-GAN: Attention-Based Varifocal Generative Adversarial Network for Uneven Medical Image Translation

Different types of staining highlight different structures in organs, thereby assisting in diagnosis. However, due to the impossibility of repeated staining, we cannot obtain different types of stained slides of the same tissue area. Translating the slide that is easy to obtain (e.g., H&E) to slides of staining types difficult to obtain (e.g., MT, PAS) is a promising way to solve this problem. However, some regions are closely connected to other regions, and to maintain this connection, they often have complex structures and are difficult to translate, which may lead to wrong translations. In this paper, we propose the Attention-Based Varifocal Generative Adversarial Network (AV-GAN), which solves multiple problems in pathologic image translation tasks, such as uneven translation difficulty in different regions, mutual interference of multiple resolution information, and nuclear deformation. Specifically, we develop an Attention-Based Key Region Selection Module, which can attend to regions with higher translation difficulty. We then develop a Varifocal Module to translate these regions at multiple resolutions. Experimental results show that our proposed AV-GAN outperforms existing image translation methods with two virtual kidney tissue staining tasks and improves FID values by 15.9 and 4.16 respectively in the H&E-MT and H&E-PAS tasks.

eess.IV↗

arXiv2023

Multi-Beholder: Biomarker Prediction for Low-Grade Glioma with Multiple Instance Learning and One-Class Classification

Biomarker detection is an indispensable part of the diagnosis and treatment of low-grade glioma (LGG). However, current LGG biomarker detection methods rely on expensive and complex molecular genetic testing, for which professionals are required to analyze the results, and intra-rater variability is often reported. To overcome these challenges, we propose an interpretable deep learning pipeline, named Multi-Biomarker Histomorphology Discoverer (Multi-Beholder), to predict the status of five biomarkers in LGG using only hematoxylin and eosin-stained whole slide images. Specifically, Multi-Beholder incorporates one-class classification into the multiple instance learning framework to achieve accurate instance-level pseudo-labeling, thereby complementing slide-level labels and improving prediction performance. Multi-Beholder demonstrates high performance on two LGG cohorts with diverse races and scanning protocols, with area under the receiver operating characteristic curve up to 0.973 on the internal-validated TCGA-LGG dataset and 0.820 on the external-validated Xiangya cohort. Moreover, the interpretability of Multi-Beholder allows for discovering quantitative and qualitative correlations between biomarker status and histomorphology characteristics. Our pipeline not only provides a novel approach for biomarker prediction, enhancing the applicability of molecular treatments for LGG patients but also facilitates the discovery of new mechanisms in molecular functionality and LGG progression. Code can be accessed at https://github.com/Vison307/Multi-Beholder.

eess.IV↗

arXiv2022

SoK: Rowhammer on Commodity Operating Systems

Rowhammer has drawn much attention from both academia and industry in the past years as rowhammer exploitation poses severe consequences to system security. Since the first comprehensive study of rowhammer in 2014, a number of rowhammer attacks have been demonstrated against dynamic random access memory (DRAM)-based commodity systems to break software confidentiality, integrity and availability. Accordingly, numerous software defenses have been proposed to mitigate rowhammer attacks on commodity systems of either legacy (e.g., DDR3) or recent DRAM (e.g., DDR4). Besides, multiple hardware defenses (e.g., Target Row Refresh) from the industry have been deployed into recent DRAM to eliminate rowhammer, which we categorize as production defenses. In this paper, we systematize rowhammer attacks and defenses with a focus on DRAM-based commodity systems. Particularly, we have established a unified framework demonstrating how a rowhammer attack affects a commodity system. With the framework, we characterize existing attacks, shedding light on new attack vectors that have not yet been explored. We further leverage the framework to categorize software and production defenses, generalize their key defense strategies and summarize their key limitations, from which potential defense strategies are identified.

cs.CR↗

arXiv2021

Paired Ru-O-Mo ensemble for efficient and stable alkaline hydrogen evolution reaction

Electrocatalytic hydrogen evolution reaction (HER) in alkaline media is a promising electrochemical energy conversion strategy. Ruthenium (Ru) is an efficient catalyst with a desirable cost for HER, however, the sluggish H2O dissociation process, due to the low H2O adsorption on its surface, currently hampers the performances of this catalyst in alkaline HER. Herein, we demonstrate that the H2O adsorption improves significantly by the construction of Ru-O-Mo sites. We prepared Ru/MoO2 catalysts with Ru-O-Mo sites through a facile thermal treatment process and assessed the creation of Ru-O-Mo interfaces by transmission electron microscope (TEM) and extended X-ray absorption fine structure (EXAFS). By using Fourier-transform infrared spectroscopy (FTIR) and H2O adsorption tests, we proved Ru-O-Mo sites have tenfold stronger H2O adsorption ability than that of Ru catalyst. The catalysts with Ru-O-Mo sites exhibited a state-of-the-art overpotential of 16 mV at 10 mA cm-2 in 1 M KOH electrolyte, demonstrating a threefold reduction than the previous bests of Ru (59 mV) and commercial Pt (31 mV) catalysts. We proved the stability of these performances over 40 hours without decline. These results could open a new path for designing efficient and stable catalysts.

physics.chem-ph↗

arXiv2021

Tuning the intermediate reaction barriers by CuPd catalyst to improve the selectivity of electroreduction CO2 to C2 products

Electrochemical CO2 reduction is a promising strategy for utilization of CO2 and intermittent excess electricity. Cu is the only single-metal catalyst that can electrochemically convert CO2 to multi-carbon products. However, Cu has an undesirable selectivity and activity for C2 products, due to its insufficient amount of CO* for C-C coupling. Considering the strong CO2 adsorption and ultra-fast reaction kinetics of CO* formation on Pd, an intimate CuPd(100) interface was designed to lower the intermediate reaction barriers and then improve the efficiency of C2 products. Density functional theory (DFT) calculations showed that the CuPd(100) interface has enhanced CO2 adsorption and decreased CO2* hydrogenation energy barrier, which are beneficial for C-C coupling. The potential-determining step (PDS) barrier of CO2 to C2 products on CuPd(100) interface is 0.61 eV, which is lower than that on Cu(100) (0.72 eV). Motivated by the DFT calculation, the CuPd(100) interface catalyst was prepared by a facile chemical solution method and demonstrated by transmission electron microscope (TEM). The CO2 temperature programmed desorption (CO2-TPD) and gas sensor experiments proved the enhancements of CO2 adsorption and CO2* hydrogenation abilities on CuPd(100) interface catalyst. As a result, the obtained CuPd(100) interface catalyst exhibits a C2 Faradaic efficiency of 50.3 (+/-) 1.2% at -1.4 VRHE in 0.1 M KHCO3, which is 2.1 times higher than 23.6(+/-) 1.5% of Cu catalyst. This work provides a rational design of Cu-based electrocatalyst for multi-carbon products by fine-tuning the intermediate reaction barriers.

cond-mat.mtrl-sci↗