arXiv subjects

Xin Gu

Publications and source records attributed to Xin Gu.

At least 37 records · Page 2Linked to original sources

arXiv2025

Fast and Rigorous Modeling of Antenna--Medium Interactions Above Planar Stratified Media via the Generalized Scattering Matrix

A rigorous and computationally efficient method is presented for evaluating the reflection coefficients of antennas operating above planar layered media. The approach reformulates the problem within the framework of the antenna's generalized scattering matrix (GSM), expressed in terms of spherical vector wave functions (SVWFs). The mutual interaction between the antenna and the layered structure is modeled through spherical-to-planar vector wave transformations that incorporate the exact Fresnel reflection response of the medium, without introducing any simplifying approximations. This formulation dramatically reduces algebraic complexity and enables fast, stable numerical implementation. Excluding the one-time preprocessing required to obtain the antenna's free-space GSM, each evaluation for a given layered configuration can be completed within milliseconds -- achieving several orders of magnitude speed improvement over full-wave solvers such as FEKO, while maintaining virtually identical accuracy. The proposed framework thus provides a powerful foundation for real-time electromagnetic characterization and inverse modeling involving planar layered environments.

cs.CE↗

arXiv2025

Zero-field Anomalous Hall Effect in Bulk Single Crystal Mn3Ir

The L1_2-phase non-collinear antiferromagnet (AFM) Mn_3Ir has emerged as a pioneering platform for realizing the zero-field anomalous Hall effect (AHE), thereby catalyzing rapid advances in antiferromagnetic spintronics. Despite its significant potential, experimental investigations of the intrinsic magnetic and electronic properties of Mn_3Ir have been greatly hindered by the formidable challenges in growing bulk single crystals. Here, we report the growth of stoichiometric Mn_3Ir bulk single crystals and their characterization in terms of magnetization and the AHE. Using a high-throughput flux method, we obtained (111)-oriented hexagonal Mn_3Ir single crystals. A small AHE signal was detected, which we attribute to the coexistence of A- and B-type antiferromagnetic domains that mutually cancel the net AHE response. Our results reveal key aspects of the intrinsic magnetic properties and AHE in bulk Mn_3Ir, providing a critical material platform for the development of advanced spintronic devices.

cond-mat.mtrl-sci↗

arXiv2025

Hybrid Method of Moments and Generalized Scattering Matrix: Applications to Antennas in Radomes, Reflectors, and Implantable Media

Electromagnetic analysis of antennas embedded in or interacting with large surrounding structures poses inherent multiscale challenges: the antenna is electrically small yet geometrically detailed, while the environment is electrically large but comparatively smooth. To address this, we present a hybrid method of moments (MoM) and generalized scattering matrix (GSM) framework that achieves a clean separation between fine-scale and large-scale complexities while preserving their full mutual coupling. Antennas of arbitrary geometry can be characterized once and reused across different environments, or conversely, a given environment can be modeled once to accommodate multiple antenna designs. The framework is inherently versatile, encompassing GSM-PO and GSM + T-matrix extensions, and thus provides a unified paradigm for multiscale antenna modeling. With the large body always represented by the formulation best suited to its scale and shape, the approach combines accuracy, efficiency, and adaptability. Numerical validations on implantable antennas, radome-protected arrays, and reflector systems confirm excellent agreement with full-wave solvers while demonstrating dramatic reductions in computational cost for design and optimization.

cs.CE↗

arXiv2025

Thinking With Videos: Multimodal Tool-Augmented Reinforcement Learning for Long Video Reasoning

The video reasoning ability of multimodal large language models (MLLMs) is crucial for downstream tasks like video question answering and temporal grounding. While recent approaches have explored text-based chain-of-thought (CoT) reasoning for MLLMs, these methods often suffer from limited cross-modal interaction and increased hallucination, especially with longer videos or reasoning chains. To address these challenges, we propose Video Intelligence via Tool-Augmented Learning (VITAL), a novel end-to-end agentic video reasoning framework. With a visual toolbox, the model can densely sample new video frames on demand and generate multimodal CoT for precise long video reasoning. We observe that temporal grounding and question answering are mutually beneficial for video understanding tasks. Therefore, we construct two high-quality multi-task video reasoning datasets MTVR-CoT-72k for supervised fine-tuning and MTVR-RL-110k for reinforcement learning. Moreover, we propose a Difficulty-aware Group Relative Policy Optimization algorithm (DGRPO) to mitigate difficulty imbalance in multi-task reinforcement learning. Extensive experiments on 11 challenging video understanding benchmarks demonstrate the advanced reasoning ability of VITAL, outperforming existing methods in video question answering and temporal grounding tasks, especially in long video scenarios. Code is available at https://zhang9302002.github.io/thinkingwithvideos-page/.

cs.CV↗

arXiv2025

Generalized Scattering Matrix Synthesis: Independent Region Decomposition for Hybrid Antenna--Scatterer Systems

This paper presents a unified formulation for synthesizing the generalized scattering matrix (GS-matrix) of hybrid electromagnetic systems comprising arbitrary numbers of antennas and scatterers. The proposed method provides a modular region decomposition framework that enables efficient analysis of electromagnetic interactions between distinct structures, under the relaxed geometric condition that the constituents are separable by a plane. By leveraging the addition theorem of vector spherical wavefunctions (VSWFs), a compact matrix representation is derived to assemble the GS- and S-matrices of individual components into the overall system response. This formulation generalizes and extends prior methods developed for either multiple scattering or antenna array analysis, and is particularly suited to configurations where substructures may be repositioned or reused. Numerical examples are provided to validate the accuracy and versatility of the method, including scenarios involving tightly spaced components and rotational variations in substructure layout.

cs.CE↗

arXiv2025

Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance

Differentially private stochastic gradient descent privatizes model training by injecting noise into each iteration, where the noise magnitude increases with the number of model parameters. Recent works suggest that we can reduce the noise by leveraging public data for private machine learning, by projecting gradients onto a subspace prescribed by the public data. However, given a choice of public datasets, it is not a priori clear which one may be most appropriate for the private task. We give an algorithm for selecting a public dataset by measuring a low-dimensional subspace distance between gradients of the public and private examples. We provide theoretical analysis demonstrating that the excess risk scales with this subspace distance. This distance is easy to compute and robust to modifications in the setting. Empirical evaluation shows that trained model accuracy is monotone in this distance.

stat.ML↗

arXiv2025

Vidi: Large Multimodal Models for Video Understanding and Editing

Humans naturally share information with those they are connected to, and video has become one of the dominant mediums for communication and expression on the Internet. To support the creation of high-quality large-scale video content, a modern pipeline requires a comprehensive understanding of both the raw input materials (e.g., the unedited footage captured by cameras) and the editing components (e.g., visual effects). In video editing scenarios, models must process multiple modalities (e.g., vision, audio, text) with strong background knowledge and handle flexible input lengths (e.g., hour-long raw videos), which poses significant challenges for traditional models. In this report, we introduce Vidi, a family of Large Multimodal Models (LMMs) for a wide range of video understand editing scenarios. The first release focuses on temporal retrieval, i.e., identifying the time ranges within the input videos corresponding to a given text query, which plays a critical role in intelligent editing. The model is capable of processing hour-long videos with strong temporal understanding capability, e.g., retrieve time ranges for certain queries. To support a comprehensive evaluation in real-world scenarios, we also present the VUE-TR benchmark, which introduces five key advancements. 1) Video duration: significantly longer than videos of existing temporal retrival datasets, 2) Audio support: includes audio-based queries, 3) Query format: diverse query lengths/formats, 4) Annotation quality: ground-truth time ranges are manually annotated. 5) Evaluation metric: a refined IoU metric to support evaluation over multiple time ranges. Remarkably, Vidi significantly outperforms leading proprietary models, e.g., GPT-4o and Gemini, on the temporal retrieval task, indicating its superiority in video editing scenarios.

cs.CV↗

arXiv2025

SuperEdit: Rectifying and Facilitating Supervision for Instruction-Based Image Editing

Due to the challenges of manually collecting accurate editing data, existing datasets are typically constructed using various automated methods, leading to noisy supervision signals caused by the mismatch between editing instructions and original-edited image pairs. Recent efforts attempt to improve editing models through generating higher-quality edited images, pre-training on recognition tasks, or introducing vision-language models (VLMs) but fail to resolve this fundamental issue. In this paper, we offer a novel solution by constructing more effective editing instructions for given image pairs. This includes rectifying the editing instructions to better align with the original-edited image pairs and using contrastive editing instructions to further enhance their effectiveness. Specifically, we find that editing models exhibit specific generation attributes at different inference steps, independent of the text. Based on these prior attributes, we define a unified guide for VLMs to rectify editing instructions. However, there are some challenging editing scenarios that cannot be resolved solely with rectified instructions. To this end, we further construct contrastive supervision signals with positive and negative instructions and introduce them into the model training using triplet loss, thereby further facilitating supervision effectiveness. Our method does not require the VLM modules or pre-training tasks used in previous work, offering a more direct and efficient way to provide better supervision signals, and providing a novel, simple, and effective solution for instruction-based image editing. Results on multiple benchmarks demonstrate that our method significantly outperforms existing approaches. Compared with previous SOTA SmartEdit, we achieve 9.19% improvements on the Real-Edit benchmark with 30x less training data and 13x smaller model size.

cs.CV↗

arXiv2025

Generalized Scattering Matrix of Antenna: Moment Solution, Compression Storage and Application

This paper presents a computation method of generalized scattering matrix (GSM) based on integral equations and the method of moments (MoM), specifically designed for antennas excited through waveguide ports. By leveraging two distinct formulations -- magnetic-type and electric-type integral equations -- we establish concise algebraic relations linking the GSM directly to the impedance matrices obtained from MoM. To address practical challenges in storing GSM data across wide frequency bands and multiple antenna scenarios, we propose a efficient compression scheme. This approach alleviates memory demands by selectively storing the dominant eigencomponents that govern scattering behavior. Numerical validation examples confirm the accuracy of our method by comparisons with full-wave simulation results. Furthermore, we introduce an efficient iterative procedure to predict antenna array performance, highlighting remarkable improvements in computational speed compared to conventional numerical methods. These results collectively demonstrate the GSM framework's strong potential for antenna-array design processes.

eess.SP↗

arXiv2025

Synthesis Method for Obtaining Characteristic Modes of Multi-Structure Systems via independent Structure T-Matrix

This paper presents a novel and efficient method for characteristic mode decomposition in multi-structure systems. By leveraging the translation and rotation matrices of vector spherical wavefunctions, our approach enables the synthesis of a composite system's characteristic modes using independently computed simulations of its constituent structures. The computationally intensive translation process is simplified by decomposing it into three streamlined sub-tasks: rotation, z-axis translation, and inverse rotation, collectively achieving significant improvements in computational efficiency. Furthermore, this method facilitates the exploration of structural orientation effects without incurring additional computational overhead. A series of illustrative numerical examples is provided to validate the accuracy of the proposed method and underscore its substantial advantages in both computational efficiency and practical applicability.

cs.CE↗

arXiv2025

Multi-Reward as Condition for Instruction-based Image Editing

High-quality training triplets (instruction, original image, edited image) are essential for instruction-based image editing. Predominant training datasets (e.g., InsPix2Pix) are created using text-to-image generative models (e.g., Stable Diffusion, DALL-E) which are not trained for image editing. Accordingly, these datasets suffer from inaccurate instruction following, poor detail preserving, and generation artifacts. In this paper, we propose to address the training data quality issue with multi-perspective reward data instead of refining the ground-truth image quality. 1) we first design a quantitative metric system based on best-in-class LVLM (Large Vision Language Model), i.e., GPT-4o in our case, to evaluate the generation quality from 3 perspectives, namely, instruction following, detail preserving, and generation quality. For each perspective, we collected quantitative score in $0\sim 5$ and text descriptive feedback on the specific failure points in ground-truth edited images, resulting in a high-quality editing reward dataset, i.e., RewardEdit20K. 2) We further proposed a novel training framework to seamlessly integrate the metric output, regarded as multi-reward, into editing models to learn from the imperfect training triplets. During training, the reward scores and text descriptions are encoded as embeddings and fed into both the latent space and the U-Net of the editing models as auxiliary conditions. 3) We also build a challenging evaluation benchmark with real-world images/photos and diverse editing instructions, named Real-Edit. Experiments indicate that our multi-reward conditioned model outperforms its no-reward counterpart on two popular editing pipelines, i.e., InsPix2Pix and SmartEdit. Code is released at https://github.com/bytedance/Multi-Reward-Editing.

cs.CV↗

arXiv2025

OmniSTVG: Toward Spatio-Temporal Omni-Object Video Grounding

In this paper, we propose spatio-temporal omni-object video grounding, dubbed OmniSTVG, a new STVG task that aims at localizing spatially and temporally all targets mentioned in the textual query from videos. Compared to classic STVG locating only a single target, OmniSTVG enables localization of not only an arbitrary number of text-referred targets but also their interacting counterparts in the query from the video, making it more flexible and practical in real scenarios for comprehensive understanding. In order to facilitate exploration of OmniSTVG, we introduce BOSTVG, a large-scale benchmark dedicated to OmniSTVG. Specifically, our BOSTVG consists of 10,018 videos with 10.2M frames and covers a wide selection of 287 classes from diverse scenarios. Each sequence in BOSTVG, paired with a free-form textual query, encompasses a varying number of targets ranging from 1 to 10. To ensure high quality, each video is manually annotated with meticulous inspection and refinement. To our best knowledge, BOSTVG is to date the first and the largest benchmark for OmniSTVG. To encourage future research, we introduce a simple yet effective approach, named OmniTube, which, drawing inspiration from Transformer-based STVG methods, is specially designed for OmniSTVG and demonstrates promising results. By releasing BOSTVG, we hope to go beyond classic STVG by locating every object appearing in the query for more comprehensive understanding, opening up a new direction for STVG. Our benchmark, model, and results will be released at https://github.com/JellyYao3000/OmniSTVG.

cs.CV↗

arXiv2025

Edit3K: Universal Representation Learning for Video Editing Components

This paper focuses on understanding the predominant video creation pipeline, i.e., compositional video editing with six main types of editing components, including video effects, animation, transition, filter, sticker, and text. In contrast to existing visual representation learning of visual materials (i.e., images/videos), we aim to learn visual representations of editing actions/components that are generally applied on raw materials. We start by proposing the first large-scale dataset for editing components of video creation, which covers about $3,094$ editing components with $618,800$ videos. Each video in our dataset is rendered by various image/video materials with a single editing component, which supports atomic visual understanding of different editing components. It can also benefit several downstream tasks, e.g., editing component recommendation, editing component recognition/retrieval, etc. Existing visual representation methods perform poorly because it is difficult to disentangle the visual appearance of editing components from raw materials. To that end, we benchmark popular alternative solutions and propose a novel method that learns to attend to the appearance of editing components regardless of raw materials. Our method achieves favorable results on editing component retrieval/recognition compared to the alternative solutions. A user study is also conducted to show that our representations cluster visually similar editing components better than other alternatives. Furthermore, our learned representations used to transition recommendation tasks achieve state-of-the-art results on the AutoTransition dataset. The code and dataset are available at https://github.com/GX77/Edit3K .

cs.CV↗

arXiv2025

Knowing Your Target: Target-Aware Transformer Makes Better Spatio-Temporal Video Grounding

Transformer has attracted increasing interest in STVG, owing to its end-to-end pipeline and promising result. Existing Transformer-based STVG approaches often leverage a set of object queries, which are initialized simply using zeros and then gradually learn target position information via iterative interactions with multimodal features, for spatial and temporal localization. Despite simplicity, these zero object queries, due to lacking target-specific cues, are hard to learn discriminative target information from interactions with multimodal features in complicated scenarios (\e.g., with distractors or occlusion), resulting in degradation. Addressing this, we introduce a novel Target-Aware Transformer for STVG (TA-STVG), which seeks to adaptively generate object queries via exploring target-specific cues from the given video-text pair, for improving STVG. The key lies in two simple yet effective modules, comprising text-guided temporal sampling (TTS) and attribute-aware spatial activation (ASA), working in a cascade. The former focuses on selecting target-relevant temporal cues from a video utilizing holistic text information, while the latter aims at further exploiting the fine-grained visual attribute information of the object from previous target-aware temporal cues, which is applied for object query initialization. Compared to existing methods leveraging zero-initialized queries, object queries in our TA-STVG, directly generated from a given video-text pair, naturally carry target-specific cues, making them adaptive and better interact with multimodal features for learning more discriminative information to improve STVG. In our experiments on three benchmarks, TA-STVG achieves state-of-the-art performance and significantly outperforms the baseline, validating its efficacy.

cs.CV↗

arXiv2024

Scattering-Based Characteristic Mode Theory for Structures in Arbitrary Background: Computation, Benchmarks, and Applications

This paper presents a novel approach for computing substructure characteristic modes. This method leverages electromagnetic scattering matrices and spherical wave expansion to directly decompose electromagnetic fields. Unlike conventional methods that rely on the impedance matrix generated by the method of moments (MoM), our technique simplifies the problem into a small-scale ordinary eigenvalue problem, improving numerical dynamics and computational efficiency. We have developed analytical substructure characteristic mode solutions for a scenario involving two spheres, which can serve as benchmarks for evaluating other numerical solvers. A key advantage of our method is its independence from specific MoM frameworks, allowing for the use of various numerical methods. This flexibility paves the way for substructure characteristic mode decomposition to become a universal frequency-domain technique.

physics.class-ph↗

arXiv2024

Optical frequency combs significantly spanned to broad bandwidths by an optomechanical resonance

Optical frequency comb, as a spectrum made of discrete and equally spaced spectral lines, is a light source with essential applications in modern technology. Cavity optomechanical systems were found to be a feasible candidate for realizing on-chip frequency comb with low repetition rate. However, it was difficult to increase the comb line numbers of this type of frequency combs because the mechanical oscillation amplitude of such system, which determines the frequency comb bandwidth, cannot quickly increase with pump laser power. Here, we develop a new approach to generate broadband optomechanical frequency comb by employing a different mechanism to enhance the mechanical oscillation. Two pump tones with their frequency difference matching the mechanical frequency will drive the system into a self-organized nonlinear resonance and thus tremendously transfer the energy to the mechanical resonator. As a result, more than $10000$ or even more comb lines become available under the pump laser power in the order of milliwatt. A unique feature of the self-organized resonance is the mechanical frequency locking so that, within a certain range of the frequency difference between two drive tones, the distance between comb teeth can be locked by the two drive tones and becomes independent of any change of pump power. This property guarantees a stable repetition rate of the generated frequency comb.

physics.optics↗

arXiv2024

Computation of Characteristic Mode for Regional Structure of Interconnected Metal Bodies

Existing methods for calculating substructure characteristic modes require treating interconnected metal structures as a single entity to ensure current continuity between different metal bodies. However, when these structures are treated as separate entities, existing methods exhibit inaccuracies, affecting the assessment of structural performance. To address this challenge, we propose an enhanced electromagnetic model that enables accurate characteristic mode analysis for regional structures within interconnected metal bodies. Numerical results validate the accuracy of the proposed method, and an antenna design example demonstrates its practical utility.

physics.class-ph↗

arXiv2024

Accurate and Fast Compressed Video Captioning

Existing video captioning approaches typically require to first sample video frames from a decoded video and then conduct a subsequent process (e.g., feature extraction and/or captioning model learning). In this pipeline, manual frame sampling may ignore key information in videos and thus degrade performance. Additionally, redundant information in the sampled frames may result in low efficiency in the inference of video captioning. Addressing this, we study video captioning from a different perspective in compressed domain, which brings multi-fold advantages over the existing pipeline: 1) Compared to raw images from the decoded video, the compressed video, consisting of I-frames, motion vectors and residuals, is highly distinguishable, which allows us to leverage the entire video for learning without manual sampling through a specialized model design; 2) The captioning model is more efficient in inference as smaller and less redundant information is processed. We propose a simple yet effective end-to-end transformer in the compressed domain for video captioning that enables learning from the compressed video for captioning. We show that even with a simple design, our method can achieve state-of-the-art performance on different benchmarks while running almost 2x faster than existing approaches. Code is available at https://github.com/acherstyx/CoCap.

cs.CV↗