arXiv subjects

Wenlong Li

Publications and source records attributed to Wenlong Li.

13 recordsLinked to original sources

arXiv2026

Understanding the Role of Algorithm Registers in AI Governance Through Comparative Analysis of China and the UK

Algorithm registers are increasingly being both considered and deployed as instruments in AI governance. They are often expected to deliver transparency; however, in practice their design, scope, and implementation vary substantially. Currently, we lack a holistic understanding of the potential roles that registers might play in AI governance, and how different design choices both shape and reflect those roles. This paper therefore asks how do algorithm registers differ across jurisdictions, and what do these differences reveal about their roles in AI governance? Towards this, we conduct a comparative analysis of two influential but contrasting algorithm registration mechanisms, China's Beian system and the UK's Algorithmic Transparency Recording Standard (ATRS), drawing on publicly available regulatory documents, registration guidelines, and registry data. Crucially, our analysis shows that an algorithm register, depending on its design and implementation, can serve functions beyond transparency, including pre-market approval, enabling ecosystem-level understanding, and acting as a broader regulatory infrastructure. As algorithm registries proliferate globally, we stress the importance of researchers and policymakers considering and examining the concrete governance functions that algorithm registries can perform as a result of their design and institutional context, rather than approaching them primarily through a transparency lens.

cs.CY↗

arXiv2026

A Unified Calibration Framework for Coordinate and Kinematic Parameters in Dual-Arm Robots

Precise collaboration in vision-based dual-arm robot systems requires accurate system calibration. Recent dual-robot calibration methods have achieved strong performance by simultaneously solving multiple coordinate transformations. However, these methods either treat kinematic errors as implicit noise or handle them through separated error modeling, resulting in non-negligible accumulated errors. In this paper, we present a novel framework for unified calibration of the coordinate transformations and kinematic parameters in both robot arms. Our key idea is to unify all the tightly coupled parameters within a single Lie-algebraic formulation. To this end, we construct a consolidated error model grounded in the product-of-exponentials formula, which naturally integrates the coordinate and kinematic parameters in twist forms. Our model introduces no artificial error separation and thus greatly mitigates the error propagation. In addition, we derive a closed-form analytical Jacobian from this model using Lie derivatives. By exploring the Jacobian rank property, we analyze the identifiability of all calibration parameters and show that our joint optimization is well-posed under mild conditions. This enables off-the-shelf iterative solvers to stably optimize these parameters on the manifold space. Besides, to ensure robust convergence of our joint optimization, we develop a certifiably correct algorithm for initializing the unknown coordinates. Relying on semidefinite relaxation, our algorithm can yield a reliable estimate whose near-global optimality can be verified a posteriori. Extensive experiments validate the superior accuracy of our approach over previous baselines under identical visual measurements. Meanwhile, our certifiable initialization consistently outperforms several coordinate-only baselines, proving its reliability as a starting point for joint optimization.

cs.RO↗

arXiv2025

VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Tree

Video anomaly detection (VAD) focuses on identifying anomalies in videos. Supervised methods demand substantial in-domain training data and fail to deliver clear explanations for anomalies. In contrast, training-free methods leverage the knowledge reserves and language interactivity of large pre-trained models to detect anomalies. However, the current fixed-length temporal window sampling approaches struggle to accurately capture anomalies with varying temporal spans. Therefore, we propose VADTree that utilizes a Hierarchical Granularityaware Tree (HGTree) structure for flexible sampling in VAD. VADTree leverages the knowledge embedded in a pre-trained Generic Event Boundary Detection (GEBD) model to characterize potential anomaly event boundaries. Specifically, VADTree decomposes the video into generic event nodes based on boundary confidence, and performs adaptive coarse-fine hierarchical structuring and redundancy removal to construct the HGTree. Then, the multi-dimensional priors are injected into the visual language models (VLMs) to enhance the node-wise anomaly perception, and anomaly reasoning for generic event nodes is achieved via large language models (LLMs). Finally, an inter-cluster node correlation method is used to integrate the multi-granularity anomaly scores. Extensive experiments on three challenging datasets demonstrate that VADTree achieves state-of-the-art performance in training-free settings while drastically reducing the number of sampled video segments. The code will be available at https://github.com/wenlongli10/VADTree.

cs.CV↗

arXiv2025

Excimer-Suppressed and Oxygen-Tolerant Photophysics of 'Arm-like' Substituted Pyrene Derivatives

Pyrene-functionalized materials are extensively employed in photoluminescent applications, owing to their extended {\pi}-conjugation and favorable photophysical properties. However, their luminescent performance is often attenuated by {\pi}-{\pi} stacking-driven excimer formation and molecular oxygen quenching. To mitigate these undesirable effects, a novel class of 7-tert-butylpyren-2-ol derivatives with extended 'arm-like' substituents at the 1,3-positions have been synthesized and their luminescent properties in solution have been thoroughly investigated. While the 2- and 7-positions of the pyrene core are frequently modified with hydroxyl and tert-butyl groups, this work presents the first introduction of 'arm-like' substituents at the 1,3-positions. The stretched-out 'arm-like' substituents not only introduce steric bulk to suppress excimer formation but also change the symmetry class of pyrene and modulate electron density at its 1,2,3,7-positions. These effects tune pyrene's energy levels and result in moderate (0.4) to high (0.7) fluorescence quantum yields and shorter-lived fluorescence lifetimes ranging from ca. 20 to 40 ns. These shorter lifetimes lead to a reduction of the pyrene derivatives' susceptibility to energy scavenging by molecular oxygen. In addition, the specific form of the 'arms' are important. Alkyl-containing arms and alkenyl-containing arms exhibit different decay pathways, which is reflected by their disparate nonradiative rates. Thus, the introduction of 'arm-like' modifications represents a promising approach to modulate the photophysical behaviour of an important class of polycyclic aromatic hydrocarbons, highlighting their applicability in next-generation electronic and optoelectronic systems.

physics.chem-ph↗

arXiv2025

Cavity Optomechanical Probe of Gravity Between Massive Mechanical Oscillators

Exploring gravitational interactions between objects with small masses has become increasingly timely. Concurrently, oscillators with masses ranging between milligrams and grams in cavity optomechanical systems sparked interest for probing gravity, and even investigating gravity within macroscopic quantum systems. Here we present a measurement scheme for probing gravity in a microwave optomechanical setup that incorporates periodic gravitational modulation between the test mass and the driven source mass at the milligram scale. Optomechanically induced transparency (OMIT) can be utilized to sense the gravitational interactions between test masses and source masses. Specifically, the relative variation in the height of the OMIT peak, expressed as $|1 + re^{i\phi}|^2 - 1$, where $r$ represents the ratio of the amplitude of the gravitational driving force to the radiation pressure force of the probe tone, and $\phi$ denotes their phase difference, can reach up to 2.3\% under plausible experimental conditions. This work may facilitate cavity-optomechanical probing of gravitational coupling between milligram-scale mechanical oscillators, a mass regime where quantum and gravitational effects converge.

quant-ph↗

arXiv2025

Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detection

Point cloud anomaly detection is essential for various industrial applications. The huge computation and storage costs caused by the increasing product classes limit the application of single-class unsupervised methods, necessitating the development of multi-class unsupervised methods. However, the feature similarity between normal and anomalous points from different class data leads to the feature confusion problem, which greatly hinders the performance of multi-class methods. Therefore, we introduce a multi-class point cloud anomaly detection method, named GLFM, leveraging global-local feature matching to progressively separate data that are prone to confusion across multiple classes. Specifically, GLFM is structured into three stages: Stage-I proposes an anomaly synthesis pipeline that stretches point clouds to create abundant anomaly data that are utilized to adapt the point cloud feature extractor for better feature representation. Stage-II establishes the global and local memory banks according to the global and local feature distributions of all the training data, weakening the impact of feature confusion on the establishment of the memory bank. Stage-III implements anomaly detection of test data leveraging its feature distance from global and local memory banks. Extensive experiments on the MVTec 3D-AD, Real3D-AD and actual industry parts dataset showcase our proposed GLFM's superior point cloud anomaly detection performance. The code is available at https://github.com/hustCYQ/GLFM-Multi-class-3DAD.

cs.CV↗

arXiv2024

A Robust Probability-based Joint Registration Method of Multiple Point Clouds Considering Local Consistency

In robotic inspection, joint registration of multiple point clouds is an essential technique for estimating the transformation relationships between measured parts, such as multiple blades in a propeller. However, the presence of noise and outliers in the data can significantly impair the registration performance by affecting the correctness of correspondences. To address this issue, we incorporate local consistency property into the probability-based joint registration method. Specifically, each measured point set is treated as a sample from an unknown Gaussian Mixture Model (GMM), and the registration problem is framed as estimating the probability model. By incorporating local consistency into the optimization process, we enhance the robustness and accuracy of the posterior distributions, which represent the one-to-all correspondences that directly determine the registration results. Effective closed-form solution for transformation and probability parameters are derived with Expectation-Maximization (EM) algorithm. Extensive experiments demonstrate that our method outperforms the existing methods, achieving high accuracy and robustness with the existence of noise and outliers. The code will be available at https://github.com/sulingjie/JPRLC_registration.

cs.RO↗

arXiv2024

Multi-watt long-wavelength infrared femtosecond lasers and resonant enamel ablation

High-power broadband tunable long-wavelength infrared (LWIR) femtosecond lasers operating at fingerprint wavelengths of 7-14 μm hold significant promise across a range of applications, including molecular hyperspectral imaging, strong-field light-matter interaction, and resonant tissue ablation. Here we present 6-12 μm broadband tunable parametric amplifier based on LiGaS2 or BaGa4S7, generating new record output power of 2.4 W at 7.5 μm, and 1.5 W at 9.5 μm, pumped by a simple and effective thin-square-rod Yb:YAG amplifier producing 110 W 274 fs output pulses. As a proof of concept, we showcase efficient resonant ablation and microstructure fabrication on enamel at the hydroxyapatite resonant wavelength of 9.5 μm, with a laser intensity two orders-of-magnitude lower than that required by non-resonant femtosecond lasers, which could foster more precision surgical applications with superior biosafety.

physics.optics↗

arXiv2024

Robust Point Cloud Registration in Robotic Inspection with Locally Consistent Gaussian Mixture Model

In robotic inspection of aviation parts, achieving accurate pairwise point cloud registration between scanned and model data is essential. However, noise and outliers generated in robotic scanned data can compromise registration accuracy. To mitigate this challenge, this article proposes a probability-based registration method utilizing Gaussian Mixture Model (GMM) with local consistency constraint. This method converts the registration problem into a model fitting one, constraining the similarity of posterior distributions between neighboring points to enhance correspondence robustness. We employ the Expectation Maximization algorithm iteratively to find optimal rotation matrix and translation vector while obtaining GMM parameters. Both E-step and M-step have closed-form solutions. Simulation and actual experiments confirm the method's effectiveness, reducing root mean square error by 20% despite the presence of noise and outliers. The proposed method excels in robustness and accuracy compared to existing methods.

cs.RO↗

arXiv2024

Mapping the Empirical Evidence of the GDPR (In-)Effectiveness: A Systematic Review

In the realm of data protection, a striking disconnect prevails between traditional domains of doctrinal, legal, theoretical, and policy-based inquiries and a burgeoning body of empirical evidence. Much of the scholarly and regulatory discourse remains entrenched in abstract legal principles or normative frameworks, leaving the empirical landscape uncharted or minimally engaged. Since the birth of EU data protection law, a modest body of empirical evidence has been generated but remains widely scattered and unexamined. Such evidence offers vital insights into the perception, impact, clarity, and effects of data protection measures but languishes on the periphery, inadequately integrated into the broader conversation. To make a meaningful connection, we conduct a comprehensive review and synthesis of empirical research spanning nearly three decades (1995- March 2022), advocating for a more robust integration of empirical evidence into the evaluation and review of the GDPR, while laying a methodological foundation for future empirical research.

cs.CY↗

arXiv2024

From Brussels Effect to Gravity Assists: Understanding the Evolution of the GDPR-Inspired Personal Information Protection Law in China

This paper explores the evolution of China's Personal Information Protection Law (PIPL) and situates it within the context of global data protection development. It draws inspiration from the theory of 'Brussels Effect' and provides a critical account of its application in non-Western jurisdictions, taking China as a prime example. Our objective is not to provide a comparative commentary on China's legal development but to illuminate the intricate dynamics between the Chinese law and the EU's GDPR. We argue that the trajectory of China's Personal Information Protection Law calls into question the applicability of the Brussels Effect: while the GDPR's imprint on the PIPL is evident, a deeper analysis unveils China's nuanced, non-linear adoption that diverges from many assumptions of the Brussels Effect and similar theories. The evolution of the GDPR-inspired PIPL is not as a straightforward outcome of the Brussels Effect but as a nuanced, intricate interplay of external influence and domestic dynamics. We introduce a complementary theory of 'gravity assist', which portrays China's strategic instrumentalisation of the GDPR as a template to shape its unique data protection landscape. Our theoretical framework highlights how China navigates through a patchwork of internal considerations, international standards, and strategic choices, ultimately sculpting a data protection regime that has a similar appearance to the GDPR but aligns with its distinct political, cultural and legal landscape. With a detailed historical and policy analysis of the PIPL, coupled with reasonable speculations on its future avenues, our analysis presents a pragmatic, culturally congruent approach to legal development in China. It signals a trajectory that, while potentially converging at a principled level, is likely to diverge significantly in practice [...]

cs.CY↗

arXiv2022

Flying-Qubit Control via a Three-level Atom with Tunable Waveguide Couplings

The control of flying qubits is at the core of quantum networks. As often carried by single-photon fields, the flying-qubit control involves not only their logical states but also their shapes. In this paper, we explore a variety of flying-qubit control problems using a three-level atom with time-varying tunable couplings to two input-output channels. It is shown that one can tune the couplings of a $Λ$-type atom to distribute a single photon into the two channels with arbitrary shapes, or use a $V$-type atom to catch an arbitrary-shape distributed single photon. The $Λ$-type atom can also be designed to transfer a flying qubit from one channel to the other, with both the central frequency and the photon shape being converted. With a $Ξ$-type atom, one can use the tunable coupling to shape a pair of correlated photons via cascaded emission. In all cases, analytical formulas are derived for the coupling functions to fulfil these control tasks, and their physical limitations are discussed as well. These results provide useful control protocols for high-fidelity quantum information transmission over complex quantum networks.

quant-ph↗

arXiv2018

Cosmic test of sTGC detector prototype made in China for ATLAS experiment upgrade

Following the Higgs particle discovery, the Large Hadron Collider complex will be upgraded in several phases allowing the luminosity to increase to $7 \times 10^{34}cm^{-2}s^{-1}$. In order to adapt the ATLAS detector to the higher luminosity environment after the upgrade, part of the ATLAS muon end-cap system, the Small Wheel, will be replaced by the New Small Wheel. The New Small Wheel includes two kinds of detectors: small-strip Thin Gap Chambers and Micromegas. Shandong University, part of the ATLAS collaboration, participates in the construction of the ATLAS New Small Wheel by developing, producing and testing the performance of part of the small-strip Thin Gap Chambers. This paper describes the construction and cosmic-ray testing of small-strip Thin Gap Chambers in Shandong University.

physics.ins-det↗