arXiv subjects

Yi Zhang

Publications and source records attributed to Yi Zhang.

At least 379 records · Page 21Linked to original sources

arXiv2025

On the baryon budget in the X-ray-emitting circumgalactic medium of Milky Way-mass galaxies

Recent observations with SRG/eROSITA have revealed the average X-ray surface brightness profile of the X-ray-emitting circumgalactic medium (CGM) around Milky Way (MW)-mass galaxies, offering valuable insights into the baryon mass in these systems. However, the estimation of the baryon mass depends critically on several assumptions regarding the gas density profile, temperature, metallicity, and the underlying halo mass distribution. Here, we assess how these assumptions affect the inferred baryon mass of the X-ray-emitting CGM in MW-mass galaxies, based on the stacked eROSITA signal. We find that variations in temperature profiles and uncertainties in the halo mass introduce the dominant sources of uncertainty, resulting in X-ray-emitting baryon mass estimates that vary by nearly a factor of four ($0.8-3.5\times10^{11} M_\odot$). Assumptions about metallicity contribute an additional uncertainty of approximately $50\%$. We emphasize that accurate X-ray spectral constraints on gas temperature and metallicity, along with careful modeling of halo mass uncertainty, are essential for accurately estimating the baryon mass for MW-mass galaxies. Future X-ray microcalorimeter missions will be crucial for determining the hot CGM properties and closing the baryon census at the MW-mass scale.

astro-ph.GA↗

arXiv2025

Optimizing Server Locations in Spatial Queues: Parametric and Nonparametric Bayesian Optimization

This paper presents a new model for solving the optimal server location problem in a spatial hypercube queueing model. Unlike deterministic location models, our approach accounts for server availability, varying utilization levels, and dependencies across servers. We prove that the problem is NP-hard and establish lower and upper bounds, as well as asymptotic results, by relating it to special cases of the classical $p$-Median problem. To address the computational challenge, we propose two Bayesian optimization approaches: (i) a parametric approach based on a sparse Bayesian linear model with second-order interactions, and (ii) a nonparametric approach using a Gaussian process surrogate with the $p$-Median objective as the prior mean function. We prove that both methods achieve sublinear regret and converge to the optimal solution. Numerical experiments and a case study using real-world data from the St. Paul, Minnesota, emergency response system show that our approaches consistently identify optimal solutions and outperform all baseline methods.

math.OC↗

arXiv2025

TubeRMC: Tube-conditioned Reconstruction with Mutual Constraints for Weakly-supervised Spatio-Temporal Video Grounding

Spatio-Temporal Video Grounding (STVG) aims to localize a spatio-temporal tube that corresponds to a given language query in an untrimmed video. This is a challenging task since it involves complex vision-language understanding and spatiotemporal reasoning. Recent works have explored weakly-supervised setting in STVG to eliminate reliance on fine-grained annotations like bounding boxes or temporal stamps. However, they typically follow a simple late-fusion manner, which generates tubes independent of the text description, often resulting in failed target identification and inconsistent target tracking. To address this limitation, we propose a Tube-conditioned Reconstruction with Mutual Constraints (\textbf{TubeRMC}) framework that generates text-conditioned candidate tubes with pre-trained visual grounding models and further refine them via tube-conditioned reconstruction with spatio-temporal constraints. Specifically, we design three reconstruction strategies from temporal, spatial, and spatio-temporal perspectives to comprehensively capture rich tube-text correspondences. Each strategy is equipped with a Tube-conditioned Reconstructor, utilizing spatio-temporal tubes as condition to reconstruct the key clues in the query. We further introduce mutual constraints between spatial and temporal proposals to enhance their quality for reconstruction. TubeRMC outperforms existing methods on two public benchmarks VidSTG and HCSTVG. Further visualization shows that TubeRMC effectively mitigates both target identification errors and inconsistent tracking.

cs.CV↗

arXiv2025

Bridging VLMs and Embodied Intelligence with Deliberate Practice Policy Optimization

Developing a universal and versatile embodied intelligence system presents two primary challenges: the critical embodied data bottleneck, where real-world data is scarce and expensive, and the algorithmic inefficiency of existing methods, which are resource-prohibitive. To address these limitations, we introduce Deliberate Practice Policy Optimization (DPPO), a metacognitive ``Metaloop'' training framework that dynamically alternates between supervised fine-tuning (competence expansion) and reinforcement learning (skill refinement). This enables automatic weakness identification and targeted resource allocation, specifically designed to maximize learning efficiency from sparse, finite data. Theoretically, DPPO can be formalised as a unified preference-learning framework. Empirically, training a vision-language embodied model with DPPO, referred to as Pelican-VL 1.0, yields a 20.3% performance improvement over the base model and surpasses open-source models at the 100B-parameter scale by 10.6%. We are open-sourcing both the models and code, providing the first systematic framework that alleviates the data and resource bottleneck and enables the community to build versatile embodied agents efficiently.

cs.AI↗

arXiv2025

A Data-Driven Model Predictive Control Framework for Multi-Aircraft TMA Routing Under Travel Time Uncertainty

This paper presents a closed-loop framework for conflict-free routing and scheduling of multi-aircraft in Terminal Manoeuvring Areas (TMA), aimed at reducing congestion and enhancing landing efficiency. Leveraging data-driven arrival inputs (either historical or predicted), we formulate a mixed-integer optimization model for real-time control, incorporating an extended TMA network spanning a 50-nautical-mile radius around Changi Airport. The model enforces safety separation, speed adjustments, and holding time constraints while maximizing runway throughput. A rolling-horizon Model Predictive Control (MPC) strategy enables closed-loop integration with a traffic simulator, dynamically updating commands based on real-time system states and predictions. Computational efficiency is validated across diverse traffic scenarios, demonstrating a 7-fold reduction in computation time during peak congestion compared to onetime optimization, using Singapore ADS-B dataset. Monte Carlo simulations under travel time disturbances further confirm the framework's robustness. Results highlight the approach's operational resilience and computational scalability, offering actionable decision support for Air Traffic Controller Officers (ATCOs) through real-time optimization and adaptive replanning.

eess.SY↗

arXiv2025

Electrical Modulation and Probing of Antiferromagnetism in Hybrid Multiferroic Heterostructures

The unique features of ultrafast spin dynamics and the absence of macroscopic magnetization in antiferromagnetic (AFM) materials provide a distinct route towards high-speed magnetic storage devices with low energy consumption and high integration density. However, these advantages also introduce challenges in probing and controlling AFM order, thereby restricting their practical applications. In this study, we demonstrate an all-electric control and probing of the AFM order in heavy metal (HM)/AFM insulator (AFMI) heterostructures on a ferroelectric substrate at room temperature (RT). The AFM order was detected by the anomalous Hall effect (AHE) and manipulated by the ferroelectric field effect as well as the piezoelectric effect in heterostructures of Pt/NiO/0.7Pb(Mg$_{1/3}$Nb$_{2/3}$)O$_{3}$--0.3PbTiO$_{3}$ (PMN--PT). The non-volatile control of AFM order gives rise to a 33\% modulation of AHE, which is further evidenced by synchrotron-based X-ray magnetic linear dichroism (XMLD). Combined with the $in$-$situ$ piezoelectric response of AHE, we demonstrate that ferroelectric polarization contributes mainly to the control of the AFM order. Our results are expected to have broader implications for efficient spintronic devices.

cond-mat.mtrl-sci↗

arXiv2025

RTS-Mono: A Real-Time Self-Supervised Monocular Depth Estimation Method for Real-World Deployment

Depth information is crucial for autonomous driving and intelligent robot navigation. The simplicity and flexibility of self-supervised monocular depth estimation are conducive to its role in these fields. However, most existing monocular depth estimation models consume many computing resources. Although some methods have reduced the model's size and improved computing efficiency, the performance deteriorates, seriously hindering the real-world deployment of self-supervised monocular depth estimation models in the real world. To address this problem, we proposed a real-time self-supervised monocular depth estimation method and implemented it in the real world. It is called RTS-Mono, which is a lightweight and efficient encoder-decoder architecture. The encoder is based on Lite-Encoder, and the decoder is designed with a multi-scale sparse fusion framework to minimize redundancy, ensure performance, and improve inference speed. RTS-Mono achieved state-of-the-art (SoTA) performance in high and low resolutions with extremely low parameter counts (3 M) in experiments based on the KITTI dataset. Compared with lightweight methods, RTS-Mono improved Abs Rel and Sq Rel by 5.6% and 9.8% at low resolution and improved Sq Rel and RMSE by 6.1% and 1.9% at high resolution. In real-world deployment experiments, RTS-Mono has extremely high accuracy and can perform real-time inference on Nvidia Jetson Orin at a speed of 49 FPS. Source code is available at https://github.com/ZYCheng777/RTS-Mono.

cs.CV↗

arXiv2025

Unveiling the Sources of X-ray Luminosity in DESI Galaxy Groups: Insights from the SRG/eROSITA All-Sky Survey

We use the first eROSITA all-sky survey (eRASS1) to investigate the contributions of AGN and extended gas to the total X-ray luminosity ($L_X$) of galaxy groups with different halo masses ($M_h$) at different redshifts. The presence of AGN in their central galaxies is identified using multi-wavelength catalogs, including the X-ray counterparts, the ASKAP radio catalog, and the DESI spectroscopic measurements. We apply the stacking method to obtain sufficient statistics for the X-ray surface brightness profile and the $L_X$ for groups with different central AGN properties. We find that the X-ray groups exhibit the highest $L_X$, followed by groups with QSO, radio, BPT-AGN, and non-AGN centrals. Moreover, the $L_X$ of the $M_h \lesssim 10^{13}h^{-1}M_\odot$ groups is dominated by the central AGN, while the X-ray emission from extended gas tends to be more prominent in the $M_h \gtrsim 10^{13}h^{-1}M_\odot$ groups. In groups where the AGN play a major role in X-ray emission, the contribution from extended gas is minor, resulting in significant uncertainties concerning the extended X-ray emission. When the subset containing the X-ray detected counterparts is excluded, the extended gas component becomes easier to obtain. A correlation has been identified between the X-ray luminosity of the central AGN and extended gas. However, once we account for the positional offset, their correlation becomes less prominent. Currently, the results are not conclusive enough to confirm whether there is a connection between the AGN feedback and extended gas. However, they provide a new perspective on the feedback processes in the history of group assembly.

astro-ph.GA↗

arXiv2025

Low-Level Dataset Distillation for Medical Image Enhancement

Medical image enhancement is clinically valuable, but existing methods require large-scale datasets to learn complex pixel-level mappings. However, the substantial training and storage costs associated with these datasets hinder their practical deployment. While dataset distillation (DD) can alleviate these burdens, existing methods mainly target high-level tasks, where multiple samples share the same label. This many-to-one mapping allows distilled data to capture shared semantics and achieve information compression. In contrast, low-level tasks involve a many-to-many mapping that requires pixel-level fidelity, making low-level DD an underdetermined problem, as a small distilled dataset cannot fully constrain the dense pixel-level mappings. To address this, we propose the first low-level DD method for medical image enhancement. We first leverage anatomical similarities across patients to construct the shared anatomical prior based on a representative patient, which serves as the initialization for the distilled data of different patients. This prior is then personalized for each patient using a Structure-Preserving Personalized Generation (SPG) module, which integrates patient-specific anatomical information into the distilled dataset while preserving pixel-level fidelity. For different low-level tasks, the distilled data is used to construct task-specific high- and low-quality training pairs. Patient-specific knowledge is injected into the distilled data by aligning the gradients computed from networks trained on the distilled pairs with those from the corresponding patient's raw data. Notably, downstream users cannot access raw patient data. Instead, only a distilled dataset containing abstract training information is shared, which excludes patient-specific details and thus preserves privacy.

cs.CV↗

arXiv2025

Pelican-VL 1.0: A Foundation Brain Model for Embodied Intelligence

This report presents Pelican-VL 1.0, a new family of open-source embodied brain models with parameter scales ranging from 7 billion to 72 billion. Our explicit mission is clearly stated as: To embed powerful intelligence into various embodiments. Pelican-VL 1.0 is currently the largest-scale open-source embodied multimodal brain model. Its core advantage lies in the in-depth integration of data power and intelligent adaptive learning mechanisms. Specifically, metaloop distilled a high-quality dataset from a raw dataset containing 4+ billion tokens. Pelican-VL 1.0 is trained on a large-scale cluster of 1000+ A800 GPUs, consuming over 50k+ A800 GPU-hours per checkpoint. This translates to a 20.3% performance uplift from its base model and outperforms 100B-level open-source counterparts by 10.6%, placing it on par with leading proprietary systems on well-known embodied benchmarks. We establish a novel framework, DPPO (Deliberate Practice Policy Optimization), inspired by human metacognition to train Pelican-VL 1.0. We operationalize this as a metaloop that teaches the AI to practice deliberately, which is a RL-Refine-Diagnose-SFT loop.

cs.LG↗

arXiv2025

Fragile by Design: On the Limits of Adversarial Defenses in Personalized Generation

Personalized AI applications such as DreamBooth enable the generation of customized content from user images, but also raise significant privacy concerns, particularly the risk of facial identity leakage. Recent defense mechanisms like Anti-DreamBooth attempt to mitigate this risk by injecting adversarial perturbations into user photos to prevent successful personalization. However, we identify two critical yet overlooked limitations of these methods. First, the adversarial examples often exhibit perceptible artifacts such as conspicuous patterns or stripes, making them easily detectable as manipulated content. Second, the perturbations are highly fragile, as even a simple, non-learned filter can effectively remove them, thereby restoring the model's ability to memorize and reproduce user identity. To investigate this vulnerability, we propose a novel evaluation framework, AntiDB_Purify, to systematically evaluate existing defenses under realistic purification threats, including both traditional image filters and adversarial purification. Results reveal that none of the current methods maintains their protective effectiveness under such threats. These findings highlight that current defenses offer a false sense of security and underscore the urgent need for more imperceptible and robust protections to safeguard user identity in personalized generation.

cs.CV↗

arXiv2025

Unlocking Dynamic Inter-Client Spatial Dependencies: A Federated Spatio-Temporal Graph Learning Method for Traffic Flow Forecasting

Spatio-temporal graphs are powerful tools for modeling complex dependencies in traffic time series. However, the distributed nature of real-world traffic data across multiple stakeholders poses significant challenges in modeling and reconstructing inter-client spatial dependencies while adhering to data locality constraints. Existing methods primarily address static dependencies, overlooking their dynamic nature and resulting in suboptimal performance. In response, we propose Federated Spatio-Temporal Graph with Dynamic Inter-Client Dependencies (FedSTGD), a framework designed to model and reconstruct dynamic inter-client spatial dependencies in federated learning. FedSTGD incorporates a federated nonlinear computation decomposition module to approximate complex graph operations. This is complemented by a graph node embedding augmentation module, which alleviates performance degradation arising from the decomposition. These modules are coordinated through a client-server collective learning protocol, which decomposes dynamic inter-client spatial dependency learning tasks into lightweight, parallelizable subtasks. Extensive experiments on four real-world datasets demonstrate that FedSTGD achieves superior performance over state-of-the-art baselines in terms of RMSE, MAE, and MAPE, approaching that of centralized baselines. Ablation studies confirm the contribution of each module in addressing dynamic inter-client spatial dependencies, while sensitivity analysis highlights the robustness of FedSTGD to variations in hyperparameters.

cs.LG↗

arXiv2025

PE-TSFM: Self-Supervised Time-Series Learning for Generalizable Power Converter Health Monitoring under Unseen Conditions

Data-driven health monitoring of power converters remains limited by poor generalization to unseen operating conditions. This work addresses this out-of-distribution (OOD) challenge by building a domain-specific time-series foundation model (PE-TSFM) that learns representations directly from large-scale unlabeled converter data. Unlike generic TSFMs trained on broad time-series datasets, the proposed PE-TSFM is pre-trained entirely on domain data, enabling it to learn the physical relationships unique to power electronics. To further tailor the model to this domain, we introduce a dual-attention mechanism that captures both temporal patterns and inter-channel dependencies. While generic TSFMs primarily model temporal dependencies, the added channel attention captures inter-sensor physical relationships essential for converter degradation analysis. A dataset containing 141 million unlabeled timestamps from an operating power converter is used for pre-training. Experiments show that PE-TSFM achieves 92% accuracy under unseen operating conditions. In contrast, generic TSFMs achieve around 60% and conventional time-series models achieve around 40% accuracy. This result confirms the strong OOD generalization of the proposed PE-TSFM. Ablation studies further verify that the introduced channel attention mechanism significantly improves model performance. In addition, we conduct detailed studies on model scalability, hyperparameter sensitivity, and interpretability to provide a comprehensive understanding of the proposed approach.

eess.SY↗

arXiv2025

Introducing the Descriptive Parametric Model: Gaseous Profiles for Galaxies, Groups, and Clusters

We develop and present the Descriptive Parametric Model (DPM), a tool for generating profiles of gaseous halos (pressure, electron density, and metallicity) as functions of radius, halo mass, and redshift. The model assumes single-phase, spherically symmetric, volume-filling warm/hot gas. The DPM framework enables mock observations of the circumgalactic medium (CGM), group halos, and clusters across a number of wavebands including X-ray, sub-millimeter/millimeter, radio, and ultraviolet (UV). We introduce three model families calibrated to reproduce cluster profiles while having different extrapolations to the CGM -- (i) self-similar halos, (ii) a reduced gas model for lower halo masses, and (iii) a model with shallower radial slopes at lower masses. We demonstrate how our z=0.0-0.6 models perform when applied to stacked and individual X-ray emission profiles, measurements of the thermal and kinetic Sunyaev-Zel'dovich Effect, electron dispersion measures from fast radio bursts, O VI absorption, and UV-derived pressures. Our investigation supports models that remove baryons from halos more effectively and have shallower profiles at lower halo mass. We discuss biases and systematics when modelling observables using consistent hot gaseous halo models for all wavebands explored. We release the DPMhalo code to encourage the use of our framework and new formulations in future investigations. Included with the DPMhalo distribution is a set of recent observations that allow the reproduction of most plots in this paper.

astro-ph.GA↗

arXiv2025

Turbulent wind thrust control to reduce pitch motion of floating wind platforms with improved rotor operation

For the dynamic analysis of floating offshore wind turbines (FOWTs) in realistic operating environment, this paper develops a coupled aero-hydro-mooring-servo model applicable to turbulent wind and irregular sea states with high computational efficiency. A modified rotor control strategy with platform motion feedback is proposed with a novel gain-scheduling technique to mitigate the negative damping effect on platform motions and decouple the rotor dynamics from the platform dynamics for better rotor operating performance. Firstly, the performance for the bottom-fixed wind turbine in steady uniform wind is validated and the turbulent wind tests show that using the rotor-disk-averaged wind speed for control is beneficial for reducing fluctuations of wind thrust and operational parameters compared with using the hub-height wind speed. For the FOWT, the negative damping phenomenon at above-rated wind speeds when using the baseline control is demonstrated through a range of wind and wave scenarios, and effects of control strategy and turbulent wind are investigated. The results indicate that the modified control strategy eliminates the negative damping effect on the platform pitch while maintaining small variations in rotor speed. Though the control including the blade pitch compensation from platform motion feedback with a constant gain can also eliminate the negative damping effect, it produces much larger fluctuations in rotor speed and causes larger overspeed exceeding the safety threshold of 20% at large wind speeds. Compared to steady wind, turbulent wind yields significantly larger low-frequency platform responses and increases the maximum rotor speed by 7.9% to 23.7% for the wind speeds considered.

physics.flu-dyn↗

arXiv2025

GenIR: Generative Visual Feedback for Mental Image Retrieval

Vision-language models (VLMs) have shown strong performance on text-to-image retrieval benchmarks. However, bridging this success to real-world applications remains a challenge. In practice, human search behavior is rarely a one-shot action. Instead, it is often a multi-round process guided by clues in mind. That is, a mental image ranging from vague recollections to vivid mental representations of the target image. Motivated by this gap, we study the task of Mental Image Retrieval (MIR), which targets the realistic yet underexplored setting where users refine their search for a mentally envisioned image through multi-round interactions with an image search engine. Central to successful interactive retrieval is the capability of machines to provide users with clear, actionable feedback; however, existing methods rely on indirect or abstract verbal feedback, which can be ambiguous, misleading, or ineffective for users to refine the query. To overcome this, we propose GenIR, a generative multi-round retrieval paradigm leveraging diffusion-based image generation to explicitly reify the AI system's understanding at each round. These synthetic visual representations provide clear, interpretable feedback, enabling users to refine their queries intuitively and effectively. We further introduce a fully automated pipeline to generate a high-quality multi-round MIR dataset. Experimental results demonstrate that GenIR significantly outperforms existing interactive methods in the MIR scenario. This work establishes a new task with a dataset and an effective generative retrieval method, providing a foundation for future research in this direction

cs.CV↗

arXiv2025

Groupwise Registration with Physics-Informed Test-Time Adaptation on Multi-parametric Cardiac MRI

Multiparametric mapping MRI has become a viable tool for myocardial tissue characterization. However, misalignment between multiparametric maps makes pixel-wise analysis challenging. To address this challenge, we developed a generalizable physics-informed deep-learning model using test-time adaptation to enable group image registration across contrast weighted images acquired from multiple physical models (e.g., a T1 mapping model and T2 mapping model). The physics-informed adaptation utilized the synthetic images from specific physics model as registration reference, allows for transductive learning for various tissue contrast. We validated the model in healthy volunteers with various MRI sequences, demonstrating its improvement for multi-modal registration with a wide range of image contrast variability.

eess.IV↗

arXiv2025

On anomalies and fermionic unitary operators

We point out that fermionic unitary operators which anticommute among themselves appear in various situations in quantum field theories with anomalies in the Hamiltonian formalism. To illustrate, we give multiple derivations of the fact that position-dependent $U(1)$ transformations of two-dimensional theories with $U(1)$ symmetry of odd level are fermionic when the winding number is odd. We then relate this mechanism to the anomalies of the discrete $\mathbb{Z}_N \subset U(1)$ symmetry, whose description also crucially uses unitary operators which are fermionic. We also show that position-dependent $SU(2)$ transformations of four-dimensional theories with $SU(2)$ symmetry with Witten anomaly are fermionic and anticommute among themselves when the winding number is odd.

hep-th↗