arXiv subjects

Yi Yang

Publications and source records attributed to Yi Yang.

At least 379 records · Page 21Linked to original sources

arXiv2025

Finding Locally Densest Subgraphs: Convex Programming with Edge and Triangle Density

Finding the densest subgraph (DS) from a graph is a fundamental problem in graph databases. The DS obtained, which reveals closely related entities, has been found to be useful in various application domains such as e-commerce, social science, and biology. However, in a big graph that contains billions of edges, it is desirable to find more than one subgraph cluster that is not necessarily the densest, yet they reveal closely related vertices. In this paper, we study the locally densest subgraph (LDS), a recently proposed variant of DS. An LDS is a subgraph which is the densest among the ``local neighbors''. Given a graph $G$, a number of LDSs can be returned, which reflect different dense regions of $G$ and thus give more information than DS. The existing LDS solution suffers from low efficiency. We thus develop a convex-programming-based solution that enables powerful pruning. We also extend our algorithm to triangle-based density to solve LTDS problem. Based on current algorithms, we propose a unified framework for the LDS and LTDS problems. Extensive experiments on thirteen real large graph datasets show that our proposed algorithm is up to four orders of magnitude faster than the state-of-the-art.

cs.DB↗

arXiv2025

TAPNext: Tracking Any Point (TAP) as Next Token Prediction

Tracking Any Point (TAP) in a video is a challenging computer vision problem with many demonstrated applications in robotics, video editing, and 3D reconstruction. Existing methods for TAP rely heavily on complex tracking-specific inductive biases and heuristics, limiting their generality and potential for scaling. To address these challenges, we present TAPNext, a new approach that casts TAP as sequential masked token decoding. Our model is causal, tracks in a purely online fashion, and removes tracking-specific inductive biases. This enables TAPNext to run with minimal latency, and removes the temporal windowing required by many existing state of art trackers. Despite its simplicity, TAPNext achieves a new state-of-the-art tracking performance among both online and offline trackers. Finally, we present evidence that many widely used tracking heuristics emerge naturally in TAPNext through end-to-end training. The TAPNext model and code can be found at https://tap-next.github.io/.

cs.CV↗

arXiv2025

DreamRenderer: Taming Multi-Instance Attribute Control in Large-Scale Text-to-Image Models

Image-conditioned generation methods, such as depth- and canny-conditioned approaches, have demonstrated remarkable abilities for precise image synthesis. However, existing models still struggle to accurately control the content of multiple instances (or regions). Even state-of-the-art models like FLUX and 3DIS face challenges, such as attribute leakage between instances, which limits user control. To address these issues, we introduce DreamRenderer, a training-free approach built upon the FLUX model. DreamRenderer enables users to control the content of each instance via bounding boxes or masks, while ensuring overall visual harmony. We propose two key innovations: 1) Bridge Image Tokens for Hard Text Attribute Binding, which uses replicated image tokens as bridge tokens to ensure that T5 text embeddings, pre-trained solely on text data, bind the correct visual attributes for each instance during Joint Attention; 2) Hard Image Attribute Binding applied only to vital layers. Through our analysis of FLUX, we identify the critical layers responsible for instance attribute rendering and apply Hard Image Attribute Binding only in these layers, using soft binding in the others. This approach ensures precise control while preserving image quality. Evaluations on the COCO-POS and COCO-MIG benchmarks demonstrate that DreamRenderer improves the Image Success Ratio by 17.7% over FLUX and enhances the performance of layout-to-image models like GLIGEN and 3DIS by up to 26.8%. Project Page: https://limuloo.github.io/DreamRenderer/.

cs.CV↗

arXiv2025

Unified Vertex Motion Estimation for Integrated Video Stabilization and Stitching in Tractor-Trailer Wheeled Robots

Tractor-trailer wheeled robots need to perform comprehensive perception tasks to enhance their operations in areas such as logistics parks and long-haul transportation. The perception of these robots faces three major challenges: the asynchronous vibrations between the tractor and trailer, the relative pose change between the tractor and trailer, and the significant camera parallax caused by the large size. In this paper, we employ the Dual Independence Stabilization Motion Field Estimation method to address asynchronous vibrations between the tractor and trailer, effectively eliminating conflicting motion estimations for the same object in overlapping regions. We utilize the Random Plane-based Stitching Motion Field Estimation method to tackle the continuous relative pose changes caused by the articulated hitch between the tractor and trailer, thus eliminating dynamic misalignment in overlapping regions. Furthermore, we apply the Unified Vertex Motion Estimation method to manage the challenges posed by the tractor-trailer's large physical size, which results in severely low overlapping regions between the tractor and trailer views, thus preventing distortions in overlapping regions from exponentially propagating into non-overlapping areas. Furthermore, this framework has been successfully implemented in real tractor-trailer wheeled robots. The proposed Unified Vertex Motion Video Stabilization and Stitching method has been thoroughly tested in various challenging scenarios, demonstrating its accuracy and practicality in real-world.

cs.RO↗

arXiv2025

Characteristics of Ge-doped Multi-Mode Fibers in Total Ionizing Dose

Purpose: The fiber optical links in 850 nm band with Ge-doped multi-mode (MM) fibers are well developed for data transmission at 10 Gbps and higher. The applications in nuclear environments require radiation resistance. The characteristics of Ge-doped MM fibers are investigated for Radiation Induced Attenuation (RIA) in Total Ionizing Dose (TID). Methods: Commercial samples of Ge-doped MM fibers were irradiated in Go-60 gamma rays at dose rates of 5 to 1.4k Gy(SiO2)/hr. The fiber samples were packaged in water tanks maintained at constant temperatures in the range of -15 to 45 degC. The optical power transmitted through the fibers were recorded in irradiation, and in annealing when the source was shielded. The measurements of RIA in time are analyzed for dose rate and temperature dependences. Results: Ge-doped fiber samples of OM2 to OM4 grades were investigated for attenuation of optical power in radiation ionizing dose. Depending on the fabrication technology, two of the fiber types show radiation resistance with the RIAs of 0.2 dB/m and 0.05 dB/m, respectively, for the TID of 300 kGy(SiO2). At low dose rate of 5 Gy/hr, the RIA increases steadily and the annealing of low density ionizing defects does not cause notable deviation. At 1.4 kGy/hr the accumulated defects result to twice higher RIA during irradiation, and is worsen to a factor three in cold temperature. However, once the source is shielded the recovery is effective in a few hours. Conclusion: The telecom products of 850 nm Ge-doped MM fibers provide high speed communication in distances of a few hundred meters. The industrial fabrication methods provide fibers that can endure radiation ionizing dose for applications in nuclear instrumentation.

physics.med-ph↗

arXiv2025

EduPlanner: LLM-Based Multi-Agent Systems for Customized and Intelligent Instructional Design

Large Language Models (LLMs) have significantly advanced smart education in the Artificial General Intelligence (AGI) era. A promising application lies in the automatic generalization of instructional design for curriculum and learning activities, focusing on two key aspects: (1) Customized Generation: generating niche-targeted teaching content based on students' varying learning abilities and states, and (2) Intelligent Optimization: iteratively optimizing content based on feedback from learning effectiveness or test scores. Currently, a single large LLM cannot effectively manage the entire process, posing a challenge for designing intelligent teaching plans. To address these issues, we developed EduPlanner, an LLM-based multi-agent system comprising an evaluator agent, an optimizer agent, and a question analyst, working in adversarial collaboration to generate customized and intelligent instructional design for curriculum and learning activities. Taking mathematics lessons as our example, EduPlanner employs a novel Skill-Tree structure to accurately model the background mathematics knowledge of student groups, personalizing instructional design for curriculum and learning activities according to students' knowledge levels and learning abilities. Additionally, we introduce the CIDDP, an LLM-based five-dimensional evaluation module encompassing clarity, Integrity, Depth, Practicality, and Pertinence, to comprehensively assess mathematics lesson plan quality and bootstrap intelligent optimization. Experiments conducted on the GSM8K and Algebra datasets demonstrate that EduPlanner excels in evaluating and optimizing instructional design for curriculum and learning activities. Ablation studies further validate the significance and effectiveness of each component within the framework. Our code is publicly available at https://github.com/Zc0812/Edu_Planner

cs.AI↗

arXiv2025

Long-horizon Visual Instruction Generation with Logic and Attribute Self-reflection

Visual instructions for long-horizon tasks are crucial as they intuitively clarify complex concepts and enhance retention across extended steps. Directly generating a series of images using text-to-image models without considering the context of previous steps results in inconsistent images, increasing cognitive load. Additionally, the generated images often miss objects or the attributes such as color, shape, and state of the objects are inaccurate. To address these challenges, we propose LIGER, the first training-free framework for Long-horizon Instruction GEneration with logic and attribute self-Reflection. LIGER first generates a draft image for each step with the historical prompt and visual memory of previous steps. This step-by-step generation approach maintains consistency between images in long-horizon tasks. Moreover, LIGER utilizes various image editing tools to rectify errors including wrong attributes, logic errors, object redundancy, and identity inconsistency in the draft images. Through this self-reflection mechanism, LIGER improves the logic and object attribute correctness of the images. To verify whether the generated images assist human understanding, we manually curated a new benchmark consisting of various long-horizon tasks. Human-annotated ground truth expressions reflect the human-defined criteria for how an image should appear to be illustrative. Experiments demonstrate the visual instructions generated by LIGER are more comprehensive compared with baseline methods.

cs.LG↗

arXiv2025

Optical and Near-infrared Observations of SN 2023ixf for over 600 days after the Explosion

Context.We present a comprehensive photometric and spectroscopic study of the nearby Type II supernova (SN) 2023ixf, with our extensive observations spanning the phases from ~3 to over 600 days after the first light.\\ Aims.The aim of this study is to obtain key information on the explosion properties of SN\,2023ixf and the nature of its progenitor.\\ Methods.The observational properties of SN\,2023ixf are compared with those of a sample of Type IIP/L SNe to investigate commonalities and diversities. We conduct a detailed analysis of temporal evolution of major spectral features observed throughout different phases of the SN\,2023ixf explosion. Several interpretations are addressed through a comparison between the data and the model spectra for progenitor stars within a range of zero-age main sequence (ZAMS) masses.\\ Results.Our observations indicate that SN\,2023ixf is a transitional SN that bridges the gap between Type IIP and IIL subclasses of H-rich SNe, characterized by a relatively short plateau ($\lesssim 70$\,d) in the light curve. It shows a rather prompt spectroscopic evolution toward the nebular phase; emission lines of Na, O, H, and Ca in nebular spectra all exhibit multipeak profiles, which might be attributed to bipolar distribution of the ejecta. In particular, the H$α$ profile can be separated into two central peaked components (with a velocity of about 1500\,km\,s$^{-1}$) that is likely due to nickel-powered ejecta and two outer peak/box components (with a velocity extending up to ~8000 km\,s$^{-1}$) that can arise from interaction of the outermost ejecta with a circumstellar shell at a distance of $\sim6.2\times10^{15}$cm. The nebular-phase spectra of SN\,2023ixf show good agreement with those predicted by model spectra for progenitor stars with a ZAMS mass ranging from 15 to 19\,M${_\odot}$. A distance $D = 6.35^{+0.31}_{-0.39}$\,Mpc is estimated for M101.

astro-ph.HE↗

arXiv2025

Roadmap on Nonlocality in Photonic Materials and Metamaterials

Photonic technologies continue to drive the quest for new optical materials with unprecedented responses. A major frontier in this field is the exploration of nonlocal (spatially dispersive) materials, going beyond the local, wavevector-independent assumption traditionally made in optical material modeling. On one end, the growing interest in plasmonic, polaritonic and quantum materials has revealed naturally occurring nonlocalities, emphasizing the need for more accurate models to predict and design their optical responses. This has major implications also for topological, nonreciprocal, and time-varying systems based on these material platforms. Beyond natural materials, artificially structured materials--metamaterials and metasurfaces--can provide even stronger and engineered nonlocal effects, emerging from long-range interactions or multipolar effects. This is a rapidly expanding area in the field of photonic metamaterials, with open frontiers yet to be explored. In the case of metasurfaces, in particular, nonlocality engineering has become a powerful tool for designing strongly wavevector-dependent responses, enabling enhanced wavefront control, spatial compression, multifunctional devices, and wave-based computing. Furthermore, nonlocality and related concepts play a critical role in defining the ultimate limits of what is possible in optics, photonics, and wave physics. This Roadmap aims to survey the most exciting developments in nonlocal photonic materials, highlight new opportunities and open challenges, and chart new pathways that will drive this emerging field forward--toward new scientific discoveries and technological advancements.

cond-mat.mes-hall↗

arXiv2025

From Trial to Triumph: Advancing Long Video Understanding via Visual Context Sample Scaling and Self-reward Alignment

Multi-modal Large language models (MLLMs) show remarkable ability in video understanding. Nevertheless, understanding long videos remains challenging as the models can only process a finite number of frames in a single inference, potentially omitting crucial visual information. To address the challenge, we propose generating multiple predictions through visual context sampling, followed by a scoring mechanism to select the final prediction. Specifically, we devise a bin-wise sampling strategy that enables MLLMs to generate diverse answers based on various combinations of keyframes, thereby enriching the visual context. To determine the final prediction from the sampled answers, we employ a self-reward by linearly combining three scores: (1) a frequency score indicating the prevalence of each option, (2) a marginal confidence score reflecting the inter-intra sample certainty of MLLM predictions, and (3) a reasoning score for different question types, including clue-guided answering for global questions and temporal self-refocusing for local questions. The frequency score ensures robustness through majority correctness, the confidence-aligned score reflects prediction certainty, and the typed-reasoning score addresses cases with sparse key visual information using tailored strategies. Experiments show that this approach covers the correct answer for a high percentage of long video questions, on seven datasets show that our method improves the performance of three MLLMs.

cs.CV↗

arXiv2025

A Multiwavelength Autopsy of the Interacting IIn Supernova 2020ywx: Tracing its Progenitor Mass-Loss History for 100 Years before Death

While the subclass of interacting supernovae with narrow hydrogen emission lines (SNe IIn) consists of some of the longest-lasting and brightest SNe ever discovered, their progenitors are still not well understood. Investigating SNe IIn as they emit across the electromagnetic spectrum is the most robust way to understand the progenitor evolution before the explosion. This work presents X-Ray, optical, infrared, and radio observations of the strongly interacting Type IIn SN 2020ywx covering a period $>1200$ days after discovery. Through multiwavelength modeling, we find that the progenitor of 2020ywx was losing mass at $\sim10^{-2}$--$10^{-3} \mathrm{\,M_{\odot}\,yr^{-1}}$ for at least 100 yrs pre-explosion using the circumstellar medium (CSM) speed of 120 km/s measured from our optical and NIR spectra. Despite the similar magnitude of mass loss measured in different wavelength ranges, we find discrepancies between the X-ray and optical/radio-derived mass-loss evolution, which suggest asymmetries in the CSM. Furthermore, we find evidence for dust formation due to the combination of a growing blueshift in optical emission lines and near-infrared continuum emission which we fit with blackbodies at $\sim$ 1000 K. Based on the observed elevated mass loss over more than 100 years and the configuration of the CSM inferred from the multiwavelength observations, we invoke binary interaction as the most plausible mechanism to explain the overall mass-loss evolution. SN 2020ywx is thus a case that may support the growing observational consensus that SNe IIn mass loss is explained by binary interaction.

astro-ph.HE↗

arXiv2025

Detection of [C I] Emission in Nebular Spectra of a Peculiar Type Ia Supernova 2022pul

SN 2022pul gains special attention due to its possible origin of a super-Chandarsekhar-mass white dwarf explosion (or called a 03fg-like type Ia supernova), which shows prominent [O I], [Ne II], and [Ca II] lines in its late-time spectra taken at $\sim+$300 days after the peak brightness. In this paper, we present new optical observations for this peculiar object, extending up to over 500 days after the peak brightness. In particular, in the $t\approx+515$ days spectrum, we identified for the first time the presence of narrow emission from [C I] $λ\lambda9824, 9850$, which appears asymmetric and quite similar to the accompanied [O I] $\lambda6300$ line in strength and profile. Based on the violent merger model that accounts well for previous observations but leaves little carbon in the center of the ejecta, this carbon line can be reproduced by increasing the degree of clumping in the ejecta and setting the carbon mass the same as that of oxygen ($\sim$0.06 $M_{\odot}$) in the innermost region ($\lesssim 2000$ km s$^{-1}$). In principle, the central carbon could come from the secondary white dwarf (WD) if it is ignited when hit by the shockwave of the explosion of the primary WD and explodes as a Ca-rich supernova, whereas pure deflagration of a super-Chandarsekhar-mass WD can account for such unburnt carbon more naturally.

astro-ph.HE↗

arXiv2025

DroneSplat: 3D Gaussian Splatting for Robust 3D Reconstruction from In-the-Wild Drone Imagery

Drones have become essential tools for reconstructing wild scenes due to their outstanding maneuverability. Recent advances in radiance field methods have achieved remarkable rendering quality, providing a new avenue for 3D reconstruction from drone imagery. However, dynamic distractors in wild environments challenge the static scene assumption in radiance fields, while limited view constraints hinder the accurate capture of underlying scene geometry. To address these challenges, we introduce DroneSplat, a novel framework designed for robust 3D reconstruction from in-the-wild drone imagery. Our method adaptively adjusts masking thresholds by integrating local-global segmentation heuristics with statistical approaches, enabling precise identification and elimination of dynamic distractors in static scenes. We enhance 3D Gaussian Splatting with multi-view stereo predictions and a voxel-guided optimization strategy, supporting high-quality rendering under limited view constraints. For comprehensive evaluation, we provide a drone-captured 3D reconstruction dataset encompassing both dynamic and static scenes. Extensive experiments demonstrate that DroneSplat outperforms both 3DGS and NeRF baselines in handling in-the-wild drone imagery.

cs.CV↗

arXiv2025

From Image to Video: An Empirical Study of Diffusion Representations

Diffusion models have revolutionized generative modeling, enabling unprecedented realism in image and video synthesis. This success has sparked interest in leveraging their representations for visual understanding tasks. While recent works have explored this potential for image generation, the visual understanding capabilities of video diffusion models remain largely uncharted. To address this gap, we systematically compare the same model architecture trained for video versus image generation, analyzing the performance of their latent representations on various downstream tasks including image classification, action recognition, depth estimation, and tracking. Results show that video diffusion models consistently outperform their image counterparts, though we find a striking range in the extent of this superiority. We further analyze features extracted from different layers and with varying noise levels, as well as the effect of model size and training budget on representation and generation quality. This work marks the first direct comparison of video and image diffusion objectives for visual understanding, offering insights into the role of temporal information in representation learning.

cs.CV↗

arXiv2025

R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization

Large Language Models have demonstrated remarkable reasoning capability in complex textual tasks. However, multimodal reasoning, which requires integrating visual and textual information, remains a significant challenge. Existing visual-language models often struggle to effectively analyze and reason visual content, resulting in suboptimal performance on complex reasoning tasks. Moreover, the absence of comprehensive benchmarks hinders the accurate assessment of multimodal reasoning capabilities. In this paper, we introduce R1-Onevision, a multimodal reasoning model designed to bridge the gap between visual perception and deep reasoning. To achieve this, we propose a cross-modal reasoning pipeline that transforms images into formal textural representations, enabling precise language-based reasoning. Leveraging this pipeline, we construct the R1-Onevision dataset which provides detailed, step-by-step multimodal reasoning annotations across diverse domains. We further develop the R1-Onevision model through supervised fine-tuning and reinforcement learning to cultivate advanced reasoning and robust generalization abilities. To comprehensively evaluate multimodal reasoning performance across different grades, we introduce R1-Onevision-Bench, a benchmark aligned with human educational stages, covering exams from junior high school to university and beyond. Experimental results show that R1-Onevision achieves state-of-the-art performance, outperforming models such as GPT-4o and Qwen2.5-VL on multiple challenging multimodal reasoning benchmarks.

cs.CV↗

arXiv2025

Evaluating Global Geo-alignment for Precision Learned Autonomous Vehicle Localization using Aerial Data

Recently there has been growing interest in the use of aerial and satellite map data for autonomous vehicles, primarily due to its potential for significant cost reduction and enhanced scalability. Despite the advantages, aerial data also comes with challenges such as a sensor-modality gap and a viewpoint difference gap. Learned localization methods have shown promise for overcoming these challenges to provide precise metric localization for autonomous vehicles. Most learned localization methods rely on coarsely aligned ground truth, or implicit consistency-based methods to learn the localization task -- however, in this paper we find that improving the alignment between aerial data and autonomous vehicle sensor data at training time is critical to the performance of a learning-based localization system. We compare two data alignment methods using a factor graph framework and, using these methods, we then evaluate the effects of closely aligned ground truth on learned localization accuracy through ablation studies. Finally, we evaluate a learned localization system using the data alignment methods on a comprehensive (1600km) autonomous vehicle dataset and demonstrate localization error below 0.3m and 0.5$^{\circ}$ sufficient for autonomous vehicle applications.

cs.RO↗

arXiv2025

DIFFVSGG: Diffusion-Driven Online Video Scene Graph Generation

Top-leading solutions for Video Scene Graph Generation (VSGG) typically adopt an offline pipeline. Though demonstrating promising performance, they remain unable to handle real-time video streams and consume large GPU memory. Moreover, these approaches fall short in temporal reasoning, merely aggregating frame-level predictions over a temporal context. In response, we introduce DIFFVSGG, an online VSGG solution that frames this task as an iterative scene graph update problem. Drawing inspiration from Latent Diffusion Models (LDMs) which generate images via denoising a latent feature embedding, we unify the decoding of object classification, bounding box regression, and graph generation three tasks using one shared feature embedding. Then, given an embedding containing unified features of object pairs, we conduct a step-wise Denoising on it within LDMs, so as to deliver a clean embedding which clearly indicates the relationships between objects. This embedding then serves as the input to task-specific heads for object classification, scene graph generation, etc. DIFFVSGG further facilitates continuous temporal reasoning, where predictions for subsequent frames leverage results of past frames as the conditional inputs of LDMs, to guide the reverse diffusion process for current frames. Extensive experiments on three setups of Action Genome demonstrate the superiority of DIFFVSGG.

cs.CV↗

arXiv2025

SN 2023ixf in the Pinwheel Galaxy M101: From Shock Breakout to the Nebular Phase

We present photometric and spectroscopic observations of SN 2023ixf covering from day one to 442 days after explosion. SN 2023ixf reached a peak $V$-band absolute magnitude of $-18.2 \pm 0.07$, and light curves show that it is in the fast-decliner (IIL) subclass with a relatively short ``plateau'' phase (fewer than $\sim 70$ days). Early-time spectra of SN 2023ixf exhibit strong, very narrow emission lines from ionized circumstellar matter (CSM), possibly indicating a Type IIn classification. But these flash/shock-ionization emission features faded after the first week and the spectrum evolved in a manner similar to that of typical Type II SNe, unlike the case of most genuine SNe~IIn in which the ejecta interact with CSM for an extended period of time and develop intermediate-width emission lines. We compare observed spectra of SN 2023ixf with various model spectra to understand the physics behind SN 2023ixf. Our nebular spectra (between 200-400 d) match best with the model spectra from a 15 $\rm M_{\odot}$ progenitor which experienced enhanced mass loss a few years before explosion. A last-stage mass-loss rate of $\dot{M} = 0.01 \rm M_{\odot} yr^{-1}$ from the r1w6 model matches best with the early-time spectra, higher than $\dot{M} \approx 2.4 \times 10^{-3} \rm M_{\odot} yr^{-1}$ derived from the ionized H$α$ luminosity at 1.58 d. We also use SN 2023ixf as a distance indicator and fit the light curves to derive the Hubble constant by adding SN 2023ixf to the existing sample; we obtain H$_{0}=73.1^{+3.68}_{-3.50}$ km s$^{-1}$ Mpc$^{-1}$, consistent with the results from SNe~Ia and many other independent methods.

astro-ph.GA↗