arXiv subjects

Ming Yang

Publications and source records attributed to Ming Yang.

At least 109 records · Page 6Linked to original sources

arXiv2025

Iterative Joint Detection of Kalman Filter and Channel Decoder for Sensor-to-Controller Link in Wireless Networked Control Systems

In this letter, we propose an iterative joint detection algorithm of Kalman filter (KF) and channel decoder for the sensor-to-controller link of wireless networked control systems, which utilizes the prior information of control system to improve control and communication performance. In this algorithm, we first use the KF to estimate the probability density of the control system outputs and calculate the prior probability of received signals to assist decoder. Then, the possible outputs of the control system are traversed to update the prior probability in order to implement iterative detection. The simulation results show that the prior information and the iterative structure can reduce the block error rate performance of communications while improving the root mean square error performance of controls.

cs.IT↗

arXiv2025

Coalescence of multiple topological orders in quasi-one-dimensional bismuth halide chains

Topology is being widely adopted to understand and to categorize quantum matter in modern physics. The nexus of topology orders, which engenders distinct quantum phases with benefits to both fundamental research and practical applications for future quantum devices, can be driven by topological phase transition through modulating intrinsic or extrinsic ordering parameters. The conjoined topology, however, is still elusive in experiments due to the lack of suitable material platforms. Here we use scanning tunneling microscopy, angle-resolved photoemission spectroscopy, and theoretical calculations to investigate the doping-driven band structure evolution of a quasi-one-dimensional material system, bismuth halide, which contains rare multiple band inversions in two time-reversal-invariant momenta. According to the unique bulk-boundary correspondence in topological matter, we unveil a composite topological phase, the coexistence of a strong topological phase and a high-order topological phase, evoked by the band inversion associated with topological phase transition in this system. Moreover, we reveal multiple-stage topological phase transitions by varying the halide element ratio: from high-order topology to weak topology, the unusual dual topology, and trivial/weak topology subsequently. Our results not only realize an ideal material platform with composite topology, but also provide an insightful pathway to establish abundant topological phases in the framework of band inversion theory.

cond-mat.mtrl-sci↗

arXiv2025

Field manipulation of Weyl modes in an ideal Dirac semimetal

The emergent Weyl modes with the broken time-reversal symmetry or inversion symmetry provide large Berry curvature and chirality to carriers, offering the realistic platforms to explore topology of electrons in three-dimensional systems. However, the reversal transition between different types of Weyl modes in a single material, which is of particular interest in the fundamental research in Weyl physics and potential application in spintronics, is scarcely achieved due to restriction of inborn symmetry in crystals. Here, by tuning the direction and strength of magnetic field in an ideal Dirac semimetal, Bi4(Br0.27I0.73)4, we report the realization of multiple Weyl modes, including gapped Weyl mode, Weyl nodal ring, and coupled Weyl mode by the magnetoresistivity measurements and electronic structure calculations. Specifically, under a magnetic field with broken mirror symmetry, anomalous Hall effect with step feature results from the large Berry curvature for the gapped Weyl mode. A prominent negative magnetoresistivity is observed at low magnetic field with preserved mirror symmetry and disappears at high magnetic field, which is correlated to the chiral anomaly and its annihilation of Weyl nodal ring, respectively. Our findings reveal distinct Weyl modes under the intertwined crystal symmetry and time-reversal breaking, laying the foundation of manipulating multiple Weyl modes in chiral spintronic network.

cond-mat.mes-hall↗

arXiv2025

StrokeNet: Unveiling How to Learn Fine-Grained Interactions in Online Handwritten Stroke Classification

Stroke classification remains challenging due to variations in writing style, ambiguous content, and dynamic writing positions. The core challenge in stroke classification is modeling the semantic relationships between strokes. Our observations indicate that stroke interactions are typically localized, making it difficult for existing deep learning methods to capture such fine-grained relationships. Although viewing strokes from a point-level perspective can address this issue, it introduces redundancy. However, by selecting reference points and using their sequential order to represent strokes in a fine-grained manner, this problem can be effectively solved. This insight inspired StrokeNet, a novel network architecture encoding strokes as reference pair representations (points + feature vectors), where reference points enable spatial queries and features mediate interaction modeling. Specifically, we dynamically select reference points for each stroke and sequence them, employing an Inline Sequence Attention (ISA) module to construct contextual features. To capture spatial feature interactions, we devised a Cross-Ellipse Query (CEQ) mechanism that clusters reference points and extracts features across varying spatial scales. Finally, a joint optimization framework simultaneously predicts stroke categories via reference points regression and adjacent stroke semantic transition modeling through an Auxiliary Branch (Aux-Branch). Experimental results show that our method achieves state-of-the-art performance on multiple public online handwritten datasets. Notably, on the CASIA-onDo dataset, the accuracy improves from 93.81$\%$ to 95.54$\%$, demonstrating the effectiveness and robustness of our approach.

cs.CV↗

arXiv2025

Learning-based 3D Reconstruction in Autonomous Driving: A Comprehensive Survey

Learning-based 3D reconstruction has emerged as a transformative technique in autonomous driving, enabling precise modeling of environments through advanced neural representations. It has inspired pioneering solutions for vital tasks in autonomous driving, such as dense mapping and closed-loop simulation, as well as comprehensive scene feature for driving scene understanding and reasoning. Given the rapid growth in related research, this survey provides a comprehensive review of both technical evolutions and practical applications in autonomous driving. We begin with an introduction to the preliminaries of learning-based 3D reconstruction to provide a solid technical background foundation, then progress to a rigorous, multi-dimensional examination of cutting-edge methodologies, systematically organized according to the distinctive technical requirements and fundamental challenges of autonomous driving. Through analyzing and summarizing development trends and cutting-edge research, we identify existing technical challenges, along with insufficient disclosure of on-board validation and safety verification details in the current literature, and ultimately suggest potential directions to guide future studies.

cs.CV↗

arXiv2025

Strings at the Tip of the Cone and Black Hole Entropy From the Worldsheet: Part I

We study the nonlinear sigma model (NLSM) worldsheet action describing the motion of closed bosonic strings in the target space of a two-dimensional (2D) flat cone in polar coordinates. We calculate the cylinder partition function. We first place the cylindrical worldsheet on a rectangular lattice before taking the continuum limit. We find an integer number of string configurations on the worldsheet, which we call line defects, that run from one boundary of the cylinder to the other. We insert two sources (conical defects) at each boundary and fix the two ends of the line defect by Dirichlet boundary conditions to a point $r_c$ in target space. In target space, a line defect appears as an Susskind\&Uglum-type open string ending on $r_c$. We compute the semiclassical contribution to the off-shell cylinder amplitude by saddle point approximation. The amplitude has an interesting infrared (IR) divergence structure that depends on the given range of the cone angle. We then compute the entropy by varying the cone angle. In a particular renormalization scheme that relates the ultraviolet (UV) and to the infrared (IR) limits of the modulus integral, we find the entropy to be free of IR divergences but linearly dependent on the radial cutoff. We argue that our calculation provides a well-defined state on a constant Euclidean-time slice directly from the string worldsheet. We also study the 2D flat cone NLSM without discretization. We compute the entropy from the off-shell stationary action and show it is finite in each winding sector $W$ with a maximum at $r_c=\sqrt{α'}/|W|$. After summing over all winding sectors, it still has a finite maximum in the UV limit but for $r_c >0$.

hep-th↗

arXiv2025

End-to-end Autonomous Vehicle Following System using Monocular Fisheye Camera

The increase in vehicle ownership has led to increased traffic congestion, more accidents, and higher carbon emissions. Vehicle platooning is a promising solution to address these issues by improving road capacity and reducing fuel consumption. However, existing platooning systems face challenges such as reliance on lane markings and expensive high-precision sensors, which limits their general applicability. To address these issues, we propose a vehicle following framework that expands its capability from restricted scenarios to general scenario applications using only a camera. This is achieved through our newly proposed end-to-end method, which improves overall driving performance. The method incorporates a semantic mask to address causal confusion in multi-frame data fusion. Additionally, we introduce a dynamic sampling mechanism to precisely track the trajectories of preceding vehicles. Extensive closed-loop validation in real-world vehicle experiments demonstrates the system's ability to follow vehicles in various scenarios, outperforming traditional multi-stage algorithms. This makes it a promising solution for cost-effective autonomous vehicle platooning. A complete real-world vehicle experiment is available at https://youtu.be/zL1bcVb9kqQ.

cs.RO↗

arXiv2025

Double-Ended Palindromic Trees in Linear Time

The palindromic tree (a.k.a. eertree) is a data structure that provides access to all palindromic substrings of a string. In this paper, we propose a dynamic version of eertree, called double-ended eertree, which supports online operations on the stored string, including double-ended queue operations, counting distinct palindromic substrings, and finding the longest palindromic prefix/suffix. At the heart of our construction, we identify a new class of substring occurrences, called surfaces, that are palindromic substring occurrences that are neither prefixes nor suffixes of any other palindromic substring occurrences, which is of independent interest. Surfaces characterize the link structure of all palindromic substrings in the eertree, thereby allowing a linear-time implementation of double-ended eertrees through a linear-time maintenance of surfaces.

cs.DS↗

arXiv2025

FedeCouple: Fine-Grained Balancing of Global-Generalization and Local-Adaptability in Federated Learning

In privacy-preserving mobile network transmission scenarios with heterogeneous client data, personalized federated learning methods that decouple feature extractors and classifiers have demonstrated notable advantages in enhancing learning capability. However, many existing approaches primarily focus on feature space consistency and classification personalization during local training, often neglecting the local adaptability of the extractor and the global generalization of the classifier. This oversight results in insufficient coordination and weak coupling between the components, ultimately degrading the overall model performance. To address this challenge, we propose FedeCouple, a federated learning method that balances global generalization and local adaptability at a fine-grained level. Our approach jointly learns global and local feature representations while employing dynamic knowledge distillation to enhance the generalization of personalized classifiers. We further introduce anchors to refine the feature space; their strict locality and non-transmission inherently preserve privacy and reduce communication overhead. Furthermore, we provide a theoretical analysis proving that FedeCouple converges for nonconvex objectives, with iterates approaching a stationary point as the number of communication rounds increases. Extensive experiments conducted on five image-classification datasets demonstrate that FedeCouple consistently outperforms nine baseline methods in effectiveness, stability, scalability, and security. Notably, in experiments evaluating effectiveness, FedeCouple surpasses the best baseline by a significant margin of 4.3%.

cs.CV↗

arXiv2025

Stochastic Momentum Methods for Non-smooth Non-Convex Finite-Sum Coupled Compositional Optimization

Finite-sum Coupled Compositional Optimization (FCCO), characterized by its coupled compositional objective structure, emerges as an important optimization paradigm for addressing a wide range of machine learning problems. In this paper, we focus on a challenging class of non-convex non-smooth FCCO, where the outer functions are non-smooth weakly convex or convex and the inner functions are smooth or weakly convex. Existing state-of-the-art result face two key limitations: (1) a high iteration complexity of $O(1/ε^6)$ under the assumption that the stochastic inner functions are Lipschitz continuous in expectation; (2) reliance on vanilla SGD-type updates, which are not suitable for deep learning applications. Our main contributions are two fold: (i) We propose stochastic momentum methods tailored for non-smooth FCCO that come with provable convergence guarantees; (ii) We establish a new state-of-the-art iteration complexity of $O(1/ε^5)$. Moreover, we apply our algorithms to multiple inequality constrained non-convex optimization problems involving smooth or weakly convex functional inequality constraints. By optimizing a smoothed hinge penalty based formulation, we achieve a new state-of-the-art complexity of $O(1/ε^5)$ for finding an (nearly) $ε$-level KKT solution. Experiments on three tasks demonstrate the effectiveness of the proposed algorithms.

cs.LG↗

arXiv2025

Quantum Fisher Information With General Quantum Coherence in multi-dimensional quantum systems

Quantum metrology is a science about quantum measurements and it plays a key role in precision of quantum parameter estimation. Meanwhile, quantum coherence is an important quantum feature and quantum Fisher information (QFI) is an important indicator for precision of quantum parameter estimation. In this paper, we explore the relationship between QFI and quantum coherence in multi-dimensional quantum systems. We introduce a new concept referred to as General Quantum Coherence (GQC), which characterizes the quantum coherence and the eigenenergies of the Hamiltonian in the interaction processes. GQC captures quantum nature of high-dimensional quantum states and addresses shortcomings in coherence measurement. Additionally, we observe a stringent square relationship between GQC and QFI. This finding provides a crucial guideline for improving the precision of parameter estimation.

quant-ph↗

arXiv2025

Which LiDAR scanning pattern is better for roadside perception: Repetitive or Non-repetitive?

LiDAR-based roadside perception is a cornerstone of advanced Intelligent Transportation Systems (ITS). While considerable research has addressed optimal LiDAR placement for infrastructure, the profound impact of differing LiDAR scanning patterns on perceptual performance remains comparatively under-investigated. The inherent nature of various scanning modes - such as traditional repetitive (mechanical/solid-state) versus emerging non-repetitive (e.g. prism-based) systems - leads to distinct point cloud distributions at varying distances, critically dictating the efficacy of object detection and overall environmental understanding. To systematically investigate these differences in infrastructure-based contexts, we introduce the "InfraLiDARs' Benchmark," a novel dataset meticulously collected in the CARLA simulation environment using concurrently operating infrastructure-based LiDARs exhibiting both scanning paradigms. Leveraging this benchmark, we conduct a comprehensive statistical analysis of the respective LiDAR scanning abilities and evaluate the impact of these distinct patterns on the performance of various leading 3D object detection algorithms. Our findings reveal that non-repetitive scanning LiDAR and the 128-line repetitive LiDAR were found to exhibit comparable detection performance across various scenarios. Despite non-repetitive LiDAR's limited perception range, it's a cost-effective option considering its low price. Ultimately, this study provides insights for setting up roadside perception system with optimal LiDAR scanning patterns and compatible algorithms for diverse roadside applications, and publicly releases the "InfraLiDARs' Benchmark" dataset to foster further research.

cs.CV↗

arXiv2025

Ming-UniAudio: Speech LLM for Joint Understanding, Generation and Editing with Unified Representation

Existing speech models suffer from competing requirements on token representations by understanding and generation tasks. This discrepancy in representation prevents speech language models from performing instruction-based free-form editing. To solve this challenge, we introduce a novel framework that unifies speech understanding, generation, and editing. The core of our unified model is a unified continuous speech tokenizer MingTok-Audio, the first continuous tokenizer to effectively integrate semantic and acoustic features, which makes it suitable for both understanding and generation tasks. Based on this unified continuous audio tokenizer, we developed the speech language model Ming-UniAudio, which achieved a balance between generation and understanding capabilities. Ming-UniAudio sets new state-of-the-art (SOTA) records on 8 out of 12 metrics on the ContextASR benchmark. Notably, for Chinese voice cloning, it achieves a highly competitive Seed-TTS-WER of 0.95. Leveraging this foundational model, we further trained a dedicated speech editing model Ming-UniAudio-Edit, the first speech language model that enables universal, free-form speech editing guided solely by natural language instructions, handling both semantic and acoustic modifications without timestamp condition. To rigorously assess the editing capability and establish a foundation for future research, we introduce Ming-Freeform-Audio-Edit, the first comprehensive benchmark tailored for instruction-based free-form speech editing, featuring diverse scenarios and evaluation dimensions spanning semantic correctness, acoustic quality, and instruction alignment. We open-sourced the continuous audio tokenizer, the unified foundational model, and the free-form instruction-based editing model to facilitate the development of unified audio understanding, generation, and manipulation.

cs.CL↗

arXiv2025

ADP-VRSGP: Decentralized Learning with Adaptive Differential Privacy via Variance-Reduced Stochastic Gradient Push

Differential privacy is widely employed in decentralized learning to safeguard sensitive data by introducing noise into model updates. However, existing approaches that use fixed-variance noise often degrade model performance and reduce training efficiency. To address these limitations, we propose a novel approach called decentralized learning with adaptive differential privacy via variance-reduced stochastic gradient push (ADP-VRSGP). This method dynamically adjusts both the noise variance and the learning rate using a stepwise-decaying schedule, which accelerates training and enhances final model performance while providing node-level personalized privacy guarantees. To counteract the slowed convergence caused by large-variance noise in early iterations, we introduce a progressive gradient fusion strategy that leverages historical gradients. Furthermore, ADP-VRSGP incorporates decentralized push-sum and aggregation techniques, making it particularly suitable for time-varying communication topologies. Through rigorous theoretical analysis, we demonstrate that ADP-VRSGP achieves robust convergence with an appropriate learning rate, significantly improving training stability and speed. Experimental results validate that our method outperforms existing baselines across multiple scenarios, highlighting its efficacy in addressing the challenges of privacy-preserving decentralized learning.

cs.LG↗

arXiv2025

GMatch: A Lightweight, Geometry-Constrained Keypoint Matcher for Zero-Shot 6DoF Pose Estimation in Robotic Grasp Tasks

6DoF object pose estimation is fundamental to robotic grasp tasks. While recent learning-based methods achieve high accuracy, their computational demands hinder deployment on resource-constrained mobile platforms. In this work, we revisit the classical keypoint matching paradigm and propose GMatch, a lightweight, geometry-constrained keypoint matcher that can run efficiently on embedded CPU-only platforms. GMatch works with keypoint descriptors and it uses a set of geometric constraints to establishes inherent ambiguities between features extracted by descriptors, thus giving a globally consistent correspondences from which 6DoF pose can be easily solved. We benchmark GMatch on the HOPE and YCB-Video datasets, where our method beats existing keypoint matchers (both feature-based and geometry-based) among three commonly used descriptors and approaches the SOTA zero-shot method on texture-rich objects with much more humble devices. The method is further deployed on a LoCoBot mobile manipulator, enabling a one-shot grasp pipeline that demonstrates high task success rates in real-world experiments. In a word, by its lightweight and white-box nature, GMatch offers a practical solution for resource-limited robotic systems, and although currently bottlenecked by descriptor quality, the framework presents a promising direction towards robust yet efficient pose estimation. Code will be released soon under Mozilla Public License.

cs.CV↗

arXiv2025

Multi-level Reliable Guidance for Unpaired Multi-view Clustering

In this thesis, we address the challenging problem of unpaired multi-view clustering (UMC), which aims to achieve effective joint clustering using unpaired samples observed across multiple views. Traditional incomplete multi-view clustering (IMC) methods typically rely on paired samples to capture complementary information between views. However, such strategies become impractical in the UMC due to the absence of paired samples. Although some researchers have attempted to address this issue by preserving consistent cluster structures across views, effectively mining such consistency remains challenging when the cluster structures {with low confidence}. Therefore, we propose a novel method, Multi-level Reliable Guidance for UMC (MRG-UMC), which integrates multi-level clustering and reliable view guidance to learn consistent and confident cluster structures from three perspectives. Specifically, inner-view multi-level clustering exploits high-confidence sample pairs across different levels to reduce the impact of boundary samples, resulting in more confident cluster structures. Synthesized-view alignment leverages a synthesized-view to mitigate cross-view discrepancies and promote consistency. Cross-view guidance employs a reliable view guidance strategy to enhance the clustering confidence of poorly clustered views. These three modules are jointly optimized across multiple levels to achieve consistent and confident cluster structures. Furthermore, theoretical analyses verify the effectiveness of MRG-UMC in enhancing clustering confidence. Extensive experimental results show that MRG-UMC outperforms state-of-the-art UMC methods, achieving an average NMI improvement of 12.95\% on multi-view datasets. {The source code is available at: https://anonymous.4open.science/r/MRG-UMC-5E20.

cs.CV↗

arXiv2025

Ming-UniVision: Joint Image Understanding and Generation with a Unified Continuous Tokenizer

Visual tokenization remains a core challenge in unifying visual understanding and generation within the autoregressive paradigm. Existing methods typically employ tokenizers in discrete latent spaces to align with the tokens from large language models, where the quantization errors can limit semantic expressiveness and degrade the capability of vision-language understanding. To address this, we introduce MingTok, a new family of visual tokenizers with a continuous latent space, for unified autoregressive generation and understanding. While understanding tasks favor discriminative high-dimensional features, generation tasks prefer compact low-level codes. Thus, to reconcile these competing demands, MingTok adopts a three-stage sequential architecture involving low-level encoding, semantic expansion, and visual reconstruction. Built on top of it, Ming-UniVision eliminates the need for task-specific visual representations, and unifies diverse vision-language tasks under a single autoregrsssive prediction paradigm. By formulating both understanding and generation as next-token prediction in a shared continuous space, it seamlessly supports multi-round, in-context tasks such as iterative understanding, generation and editing. Empirically, we find that using a unified continuous visual representation reconciles the competing requirements on the tokenizers by the understanding and generation tasks, thereby leading to state-of-the-art level performance across both domains. We hope our findings will facilitate unified visual tokenization in the continuous domain. Inference code and model weights are released to benefit community.

cs.CV↗

arXiv2025

A decade of transit photometry for K2-19: Revised system architecture

The star K2-19 hosts a pair of Neptunian planets deep inside the 3:2 resonance. They induce strong transit-timing variations with two incommensurate frequencies. Previous photodynamical modeling of 3.3 years of transit and radial velocity data produced mass estimates of 32.4 +/- 1.7 M_E and 10.8 +/- 0.6 M_E for planets b and c, respectively, and corresponding eccentricity estimates of 0.20 +/- 0.03 and 0.21 +/- 0.03. These high eccentricities raise questions about the formation origin of the system, and this motivated us to extend the observing baseline in an attempt to better constrain their values. We present a photodynamical analysis of 10 years of transit data that confirms the previous mass estimates (30.8 +/- 1.3 M_E and 11.1 +/- 0.4 M_E), but reduces the median eccentricities to 0.04 +/- 0.02 and 0.07 +/- 0.02 for b and c, respectively. These values are more consistent with standard formation models, but still involve nonzero free eccentricity. The previously reported high eccentricities appear to be due to a single transit for which measurements taken at twilight mimicked ingress. This resulted in a 12-minute error in the midtransit time. The data that covered 1.3 and 5 so-called super and resonant periods were used to match a Fourier analysis of the transit-timing variation signal with simple analytic expressions for the frequencies and amplitudes to obtain planet mass estimates within 2% of the median photodynamical values, regardless of the eccentricities. Theoretical details of the analysis are presented in a companion paper. Additionally, we identified a possible planet candidate situated exterior to the b-c pair. Finally, in contrast to a previous study, our internal structure modeling of K2-19 b yields a metal mass fraction that is consistent with core accretion.

astro-ph.EP↗