arXiv subjects

Haodong Jiang

Publications and source records attributed to Haodong Jiang.

11 recordsLinked to original sources

arXiv2026

Preserve, Then Resolve: Many-to-Many Association and Robust Estimation with General-Purpose Visual Features

The semantic transferability of general-purpose visual features does not guarantee geometric consistency across images. Using frozen DINOv3 features, we show that geometrically correct correspondences often fall below rank one in cosine similarity yet remain within a small top-$K$ candidate set. This motivates a preserve-then-resolve design: we retain multiple candidates in a many-to-many (m-to-m) association graph and defer their disambiguation to robust estimation. We study m-to-m robust estimation from a probabilistic perspective. We interpret the existing Matching Cardinality Maximization (MCM) mechanism as a dominant-cardinality approximation to likelihood maximization and propose a faster, real-valued mechanism called Harmonic Consensus Maximization (HCM). A two-stage LO-RANSAC uses HCM for candidate sourcing and MCM for graph-aware selection. We evaluate end-task gains in relative-pose estimation, where our pipeline consistently improves rank-one baselines for DINOv2, DINOv3, V-JEPA 2.1, and SigLIP 2. Code is available at https://github.com/LIAS-CUHKSZ/preserve_then_resolve.

cs.CV↗

arXiv2026

Monolithic integration of diverse crystalline thin films on diamond for near-junction thermal management

The pursuit of extreme miniaturization and high power in 6G RF front-ends has cast thermal dissipation as the central challenge. Here, we have demonstrated the monolithic integration of functionally distinct single-crystal thin films, including \b{eta}-Ga2O3, Si, GaN, and LiTaO3, onto a single diamond substrate using a multi-step transfer printing technique. Focusing on the critical \b{eta}-Ga2O3/diamond interface, we achieve an exceptional interfacial thermal conductance (ITC) of 149 MW m-2 K-1 through ultra-high vacuum (UHV) annealing, creating an atomically sharp interface featuring covalent bonding. Vibrational electron energy-loss spectroscopy (EELS) analysis combining with molecular dynamics (MD) simulations reveal that distinctive interfacial phonon modes at the \b{eta}-Ga2O3/diamond heterointerface dominate ultrahigh ITC. We experimentally demonstrate that by improving the ITC, the thermal resistance (Rth) of a diamond-based \b{eta}-Ga2O3 MOSFET is driven to a record-low value of 1.58 K mm W-1, underscoring the critical role of interface engineering in near-junction thermal management for diamond-integrated devices. This work demonstrates a scalable, diamond-based monolithic integration platform designed to solve the near-junction thermal challenges in high-power RF front-ends.

cond-mat.mtrl-sci↗

arXiv2025

Fully Distributed State Estimation for Multi-agent Systems and its Application in Cooperative Localization

In this paper, we investigate the distributed state estimation problem for a continuous-time linear multi-agent system (MAS) composed of $\mathit{m}$ agents and monitored by the agents themselves. To address this problem, we propose a distributed observer that enables each agent to reconstruct the state of the MAS. The main idea is to let each agent $\mathit{i}$ recover the state of agent $\mathit{j}$ by using leader-follower consensus rules to track agent $\mathit{j}$'s state estimate, which is generated by agent $\mathit{j}$ itself using a Luenberger-like estimation rule. Under the assumptions of node-level observability and topological ordering consistency, we show that the estimation error dynamics are stabilizable if and only if the communication graph is strongly connected. Moreover, we discuss the fully distributed design of the proposed observer, assuming that the agents only know basic MAS configuration information, such as the homogeneity and the maximum number of allowable agents. This design ensures that the proposed observer functions correctly when agents are added or removed. Building on this, we consider cooperative localization as a distributed estimation problem and develop two fully distributed localization algorithms that allow agents to track their own and other agents' positions (and velocities) within the MAS. Finally, we conduct simulations to demonstrate the effectiveness of our proposed theoretical results.

eess.SY↗

arXiv2025

SCORE: Saturated Consensus Relocalization in Semantic Line Maps

We present SCORE, a visual relocalization system that achieves unprecedented map compactness by adopting semantically labeled 3D line maps. SCORE requires only 0.01\%-0.1\% of the storage needed by structure-based or learning-based baselines, while maintaining practical accuracy and comparable runtime. The key innovation is a novel robust estimation mechanism, Saturated Consensus Maximization (Sat-CM), which generalizes classical Consensus Maximization (CM) by assigning diminishing weights to inlier associations according to maximum likelihood with probabilistic justification. Under extreme outlier ratios (up to 99.5\%) arising from one-to-many ambiguity in semantic matching, Sat-CM enables accurate estimation when CM fails. To ensure computational efficiency, we propose an accelerating framework for globally solving Sat-CM formulations and specialize it for the Perspective-n-Lines problem at the core of SCORE.

cs.RO↗

arXiv2025

Bilateral Differentially Private Vertical Federated Boosted Decision Trees

Federated learning is a distributed machine learning paradigm that enables collaborative training across multiple parties while ensuring data privacy. Gradient Boosting Decision Trees (GBDT), such as XGBoost, have gained popularity due to their high performance and strong interpretability. Therefore, there has been a growing interest in adapting XGBoost for use in federated settings via cryptographic techniques. However, it should be noted that these approaches may not always provide rigorous theoretical privacy guarantees, and they often come with a high computational cost in terms of time and space requirements. In this paper, we propose a variant of vertical federated XGBoost with bilateral differential privacy guarantee: MaskedXGBoost. We build well-calibrated noise to perturb the intermediate information to protect privacy. The noise is structured with part of its ingredients in the null space of the arithmetical operation for splitting score evaluation in XGBoost, helping us achieve consistently better utility than other perturbation methods and relatively lower overhead than encryption-based techniques. We provide theoretical utility analysis and empirically verify privacy preservation. Compared with other algorithms, our algorithm's superiority in both utility and efficiency has been validated on multiple datasets.

cs.CR↗

arXiv2025

Inteval Analysis for two spherical functions arising from robust Perspective-n-Lines problem

This report presents a comprehensive interval analysis of two spherical functions derived from the robust Perspective-n-Lines (PnL) problem. The study is motivated by the application of a dimension-reduction technique to achieve global solutions for the robust PnL problem. We establish rigorous theoretical results, supported by detailed proofs, and validate our findings through extensive numerical simulations.

cs.CV↗

arXiv2023

Efficient Planar Pose Estimation via UWB Measurements

State estimation is an essential part of autonomous systems. Integrating the Ultra-Wideband(UWB) technique has been shown to correct the long-term estimation drift and bypass the complexity of loop closure detection. However, few works on robotics adopt UWB as a stand-alone state estimation solution. The primary purpose of this work is to investigate planar pose estimation using only UWB range measurements and study the estimator's statistical efficiency. We prove the excellent property of a two-step scheme, which says that we can refine a consistent estimator to be asymptotically efficient by one step of Gauss-Newton iteration. Grounded on this result, we design the GN-ULS estimator and evaluate it through simulations and collected datasets. GN-ULS attains millimeter and sub-degree level accuracy on our static datasets and attains centimeter and degree level accuracy on our dynamic datasets, presenting the possibility of using only UWB for real-time state estimation.

cs.RO↗

arXiv2022

Closed-form Error Propagation on the SE_n(3) Group for Invariant Extended Kalman Filtering with Applications to VINS

Pose estimation is important for robotic perception, path planning, etc. Robot poses can be modeled on matrix Lie groups and are usually estimated via filter-based methods. In this paper, we establish the closed-form formula for the error propagation for the Invariant extended Kalman filter (IEKF) in the presence of random noises and apply it to vision-aided inertial navigation. We evaluate our algorithm via numerical simulations and experiments on the OPENVINS platform. Both simulations and the experiments performed on the public EuRoC MAV datasets demonstrate that our algorithm outperforms some state-of-art filter-based methods such as the quaternion-based EKF, first estimates Jacobian EKF, etc.

cs.RO↗

arXiv2016

Universally-composable finite-key analysis for efficient four-intensity decoy-state quantum key distribution

We propose an efficient four-intensity decoy-state BB84 protocol and derive concise security bounds for this protocol with the universally composable finite-key analysis method. Comparing with the efficient three-intensity protocol, we find that our efficient four-intensity protocol can increase the secret key rate by at least $30\%$. Particularly, this increasing rate of secret key rate will be raised as the transmission distance increases. At a large transmission distance, our efficient four-intensity protocol can improve the performance of quantum key distribution profoundly.

quant-ph↗

arXiv2015

Stringent Statistical Fluctuation Analysis for Quantum Key Distribution Considering After-pulse Contributions

Statistical fluctuation problems are faced by all quantum key distribution (QKD) protocols under finite-key condition. Most of the current statistical fluctuation analysis methods work based on independent random samples, however, the precondition cannot be always satisfied on account of different choice of samples and actual parameters. As a result, proper statistical fluctuation methods are required to figure out this problem. Taking the after-pulse contributions into consideration, we give the expression of secure key rate and the mathematical model for statistical fluctuations, focusing on a decoy-state QKD protocol (Sci Rep. 3, 2453, 2013) with biased basis choice. On this basis, a classified analysis of statistical fluctuation is represented according to the mutual relationship between random samples. First for independent identical relations, we make a deviation comparison between law of large numbers and standard error analysis. Secondly, we give a sufficient condition that Chernoff bound achieves a better result than Hoeffding's inequality based on independent relations only. Thirdly, by constructing the proper martingale, for the first time we represent a stringent way to deal with statistical fluctuation issues upon dependent ones through making use of Azuma's inequality. In numerical optimization, we show the impact on secure key rate, the ones and respective deviations under various kinds of statistical fluctuation analyses.

quant-ph↗

arXiv2015

A global estimation of the lower bound of the privacy amplification term for decoy-state quantum key distribution

The privacy amplification term, of which the lower bound needs to be estimated with the decoy-state method, plays a positive role in the secure key rate formula for decoy-state quantum key distribution. In previous work, the yield and the bit error rate of single-photon state are estimated separately to gain this lower bound. In this work, we for the first time take the privacy amplification term as a whole to consider this lower bound. The mathematical description for the correlation between the yield and the bit error rate of single-photon state is given with just two unknown variables. Based on this, we obtain the global estimation of this lower bound for both BB84 protocol and measurement-device-independent protocol. The results of numerical simulation show that the global estimation can significantly improve the performance of quantum key distribution.

quant-ph↗