arXiv subjects

Chang Shu

Publications and source records attributed to Chang Shu.

At least 19 recordsLinked to original sources

arXiv2026

Coverage Planning for Robotic Tooth Preparation in Densely Constrained Environments

Tooth preparation refers to the controlled removal of tooth structure to create an optimal substrate for fixed restorations and is a core procedure in restorative dentistry. Automating this task is particularly challenging for robots because the dental bur must operate within a densely constrained intraoral workspace, where even sub-millimeter deviations can compromise outcomes or damage adjacent structures. This paper presents a novel robotic system for autonomous full-crown tooth preparation. The proposed framework includes: 1) an anatomy-aware toolpath planning algorithm that conforms precisely to a technician-designed preparation model while protecting adjacent teeth, and 2) a clearance-oriented end-effector yaw assignment strategy that allows intraoral access while reducing the risk of soft-tissue interference. Together, these features enable the robot to accurately mill the irregular tooth surface with an average geometric deviation of 0.117 mm (RMSE), achieving both restoration quality and clinical safety. A series of simulations and phantom-head experiments validate the system's feasibility and effectiveness.

cs.RO↗

arXiv2026

From swept contact to pose: Probe-aware registration via complementary-shape docking

Accurate registration between a prior model and the real scene is essential for high-precision robotic manipulation, yet optical methods suffer from long calibration chains, line-of-sight constraints, and fabrication errors. We propose a calibration-free alternative that reformulates contact registration as complementary-shape docking between the object and the probe's swept volume, explicitly accounting for probe geometry and leveraging both contact and non-contact evidence. Our solver integrates a global-to-local search via 3D FFT correlation over low-discrepancy SO(3) samples, then followed by continuous SE(3) refinement using Lie-algebra updates and analytic contact sensitivities. This pipeline yields efficient exploration and metric-grade convergence without fragile point correspondences. Simulation across free-form meshes achieved sub-0.04 mm and sub-0.4{\deg} accuracy and robustness to pose noise and contact loss. On a tooth-preparation robot, our method attained 0.42 mm and 3.75{\deg}, outperforming an optical tracker registration while requiring no external sensors. These results demonstrate a practical and precise registration strategy for surgical and industrial robots.

cs.RO↗

arXiv2026

Observation of Universal Spectral Moments and the Dynamic Dispersive-to-Proliferative Transition

In non-Hermitian systems, spectra can be maximally boundary-sensitive, yet bulk physics need not be. Here we experimentally show that spectral moments provide boundary-robust bulk observables in finite non-Hermitian lattices, even when the spectra undergo dramatic geometry-dependent reshaping due to the skin effect. Using a unified acoustic platform with full spectral reconstruction and time-domain access, we probe one-, two- and three-dimensional lattices and demonstrate that spectral moments remain nearly invariant across distinct boundary geometries while the corresponding complex spectra differ strongly. To connect the thermodynamic theorem to realistic finite systems, we develop a loop-counting theory that identifies the physical origin of finite-size deviations in terms of missing boundary loops, quantitatively captures the corrections, and predicts a scaling law, which we verify experimentally. Beyond acoustic spectroscopy, we reveal a counterintuitive dynamical consequence of moment invariance: a dispersive-to-proliferative bulk transition governed by bulk moment structure rather than spectral boundary sensitivity. As a result, local bulk dynamics can remain stable (dispersive) even in a $\mathcal{PT}$-broken spectral regime, challenging the conventional expectation that $\mathcal{PT}$ breaking necessarily implies feedback-induced dynamical instability (proliferation) through exponentially amplifying spectral components. These results establish spectral moments as practical bulk descriptors for finite non-Hermitian matter and open a route to extracting and controlling intrinsic bulk behavior in realistic wave-based non-Hermitian devices.

quant-ph↗

arXiv2026

Universal Dynamical Scaling of Strong-to-Weak Spontaneous Symmetry Breaking in Open Quantum Systems

Strong-to-weak spontaneous symmetry breaking (SWSSB) defines a mixed-state phase of matter--without a pure-state counterpart--in which nonlinear observables such as the R\'enyi-2 correlator develop long-range order while conventional linear correlations remain short-ranged. Here we study the emergence of SWSSB in one-dimensional open quantum systems governed by Lindbladian evolution, where the transition time diverges with system size and SWSSB appears only asymptotically in the steady state. By tracking the late-time growth of the R\'enyi-2 correlation length, we uncover a universal dynamical regime controlled purely by the symmetry class of the Lindbladian. Contrary to the conventional expectation that late-time dynamics are governed by the low-lying Liouvillian spectrum, we find that the time dependence of the SWSSB transition--exponential versus algebraic--is dictated solely by symmetry, independent of details of the Lindbladian, including whether the Liouvillian spectrum is gapped or gapless. For $\mathbb{Z}_2$-symmetric dynamics, the R\'enyi-2 correlation length grows exponentially in time--even when the spectrum is gapless--yielding an effective transition time $t_c \propto \operatorname{ln} L$ and enabling rapid preparation of the $\mathbb{Z}_2$ SWSSB steady state. In contrast, U(1)-symmetric dynamics exhibit algebraic scaling, $t_c \propto L^{\alpha}$, with a filling-dependent dynamical exponent: ballistic growth ($\alpha \approx 1$) at finite filling crosses over to diffusive scaling ($\alpha = 2$) in the zero-filling limit. These results establish symmetry--rather than spectral gap structure--as the controlling principle for SWSSB late-time dynamical scaling, and open a new route to nonequilibrium symmetry breaking in open quantum systems.

cond-mat.mes-hall↗

arXiv2026

A Stable and General Quantum Fractional-Step Lattice Boltzmann Method for Incompressible Flows

Quantum computing shows substantial potential in accelerating simulations and alleviating memory bottlenecks in computational fluid dynamics (CFD), owing to its inherent properties of superposition and entanglement. The lattice Boltzmann method (LBM), being largely algebraic in nature, has inspired the development of various quantum LBMs. However, most existing approaches fix the relaxation time at $\tau$ = 1, thereby confining a given mesh resolution to simulations at a single Reynolds number. Although our earlier quantum lattice kinetic scheme (LKS) lifted this restriction, it suffers from instability at high Reynolds numbers. To address this challenge, we propose a quantum fractional-step LBM (FS-LBM). In this framework, the predictor step is implemented on a quantum circuit using the standard LBM formulation, while the corrector step is performed classically. The relaxation time is retained at $\tau$ = 1 to ensure seamless compatibility with existing quantum LBMs. Benchmark simulations of representative two- and three-dimensional incompressible isothermal and thermal flows demonstrate that the quantum FS-LBM achieves accuracy and convergence orders consistent with its classical counterpart, while significantly outperforming the quantum LKS in both precision and stability. Notably, this work presents the first quantum LBM simulation of three-dimensional incompressible thermal flows.

quant-ph↗

arXiv2026

Quantum Entanglement and Teleportation of Magnons in Coupled Spin Chains

This study explores how entanglement and quantum teleportation of magnons can be achieved in coupled spin chain systems. By utilizing different magnetic configurations, we show that parallel spin chains function like magnonic beam splitters, whereas anti-parallel chains produce two-magnon squeezing and strong entanglement. Combining these components, we design magnonic circuits capable of continuous-variable quantum entanglement and teleportation, supported by quantum Langevin simulations.

cond-mat.mes-hall↗

arXiv2025

SMP: Reusable Score-Matching Motion Priors for Physics-Based Character Control

Data-driven motion priors that can guide agents toward producing naturalistic behaviors play a pivotal role in creating life-like virtual characters. Adversarial imitation learning has been a highly effective method for learning motion priors from reference motion data. However, adversarial priors, with few exceptions, need to be retrained for each new controller, thereby limiting their reusability and necessitating the retention of the reference motion data when applied to downstream tasks. In this work, we present Score-Matching Motion Priors (SMP), which leverages pre-trained motion diffusion models and score distillation sampling (SDS) to create reusable task-agnostic motion priors. SMPs can be pre-trained on a motion dataset, independent of any control policy or task. Once trained, SMPs can be kept frozen and reused as general-purpose reward functions to train new policies to produce naturalistic behaviors for downstream tasks. We show that a general motion prior trained on large-scale datasets can be repurposed into a variety of style-specific priors. Furthermore, SMP can compose different styles to synthesize new styles not present in the original dataset. Our method can create reusable and modular motion priors that produce high-quality motions comparable to state-of-the-art adversarial imitation learning methods. In our experiments, we demonstrate the effectiveness of SMP across a diverse suite of control tasks with physically simulated humanoid characters. Video available at https://youtu.be/jBA2tWk6vzU

cs.GR↗

arXiv2025

SHARE: Scene-Human Aligned Reconstruction

Animating realistic character interactions with the surrounding environment is important for autonomous agents in gaming, AR/VR, and robotics. However, current methods for human motion reconstruction struggle with accurately placing humans in 3D space. We introduce Scene-Human Aligned REconstruction (SHARE), a technique that leverages the scene geometry's inherent spatial cues to accurately ground human motion reconstruction. Each reconstruction relies solely on a monocular RGB video from a stationary camera. SHARE first estimates a human mesh and segmentation mask for every frame, alongside a scene point map at keyframes. It iteratively refines the human's positions at these keyframes by comparing the human mesh against the human point map extracted from the scene using the mask. Crucially, we also ensure that non-keyframe human meshes remain consistent by preserving their relative root joint positions to keyframe root joints during optimization. Our approach enables more accurate 3D human placement while reconstructing the surrounding scene, facilitating use cases on both curated datasets and in-the-wild web videos. Extensive experiments demonstrate that SHARE outperforms existing methods.

cs.CV↗

arXiv2025

All Roads Lead to Rome: Graph-Based Confidence Estimation for Large Language Model Reasoning

Confidence estimation is essential for the reliable deployment of large language models (LLMs). Existing methods are primarily designed for factual QA tasks and often fail to generalize to reasoning tasks. To address this gap, we propose a set of training-free, graph-based confidence estimation methods tailored to reasoning tasks. Our approach models reasoning paths as directed graphs and estimates confidence by exploiting graph properties such as centrality, path convergence, and path weighting. Experiments with two LLMs on three reasoning datasets demonstrate improved confidence estimation and enhanced performance on two downstream tasks.

cs.CL↗

arXiv2025

Dynamical Quantum Phase Transitions and Many-Body Backflow in Open Quantum Systems

Dynamical quantum phase transitions (DQPTs) are non-equilibrium transitions characterized by the orthogonality between an initial quantum state and its time-evolved counterpart following a sudden quench. Recently, studies of this phenomenon have been extended beyond closed quantum systems to include environmental interactions, often modeled through non-Hermitian effects. However, because non-Hermitian descriptions neglect both quantum jump processes and interaction effects, the ultimate fate of interacting quantum systems under full open-system quantum dynamics remains an open question. In this paper, by incorporating both interactions and full Liouvillian dynamics, we prove that DQPTs in open quantum systems remain robust when subject to either particle loss or particle gain alone, but are generically smeared out when both processes coexist, as a result of many-body particle backflow. Furthermore, we uncover a non-perturbative dynamical effect: even a weak admixture of gain (loss) into a system with loss (gain) can dramatically reshape the long-time behavior of DQPT dynamics, leading to substantial deviations over time. These phenomena--including the universal smearing of DQPTs and the emergence of large dynamical deviations in the long-time limit--arise intrinsically from non-equilibrium many-body effects in open quantum systems. Our findings are general and substantiated by both analytical arguments and numerical simulations.

quant-ph↗

arXiv2025

Quantum Lattice Kinetic Scheme for Solving Two-dimensional and Three-dimensional Incompressible Flows

Lattice Boltzmann method (LBM) is particularly well-suited for implementation on quantum circuits owing to its simple algebraic operations and natural parallelism. However, most quantum LBMs fix $\tau$ = 1 to avoid nonlinear collision, which restricts the simulation to a fixed mesh size for a given Reynolds number. To preserve the simplicity of setting $\tau$ = 1 while enhancing flexibility, we propose a quantum lattice kinetic scheme (LKS) by introducing a constant parameter $A$ into the equilibrium distribution function (EDF), enabling independent adjustment of the fluid's viscosity. This modification removes the constraint on mesh size, making it possible to simulate flows with arbitrary Reynolds numbers. The Chapman-Enskog analysis confirms the modified EDF still recovers the Navier-Stokes equations without compromising collision accuracy. We evaluate the method on 2D and 3D Taylor-Green vortex and lid-driven cavity flows, demonstrating that quantum LKS attains the same accuracy and convergence order as classical LKS. The first application of quantum LBM to 3D incompressible flows represents a significant step forward in large-scale fluid dynamics simulation.

quant-ph↗

arXiv2025

StableMotion: Training Motion Cleanup Models with Unpaired Corrupted Data

Motion capture (mocap) data often exhibits visually jarring artifacts due to inaccurate sensors and post-processing. Cleaning this corrupted data can require substantial manual effort from human experts, which can be a costly and time-consuming process. Previous data-driven motion cleanup methods offer the promise of automating this cleanup process, but often require in-domain paired corrupted-to-clean training data. Constructing such paired datasets requires access to high-quality, relatively artifact-free motion clips, which often necessitates laborious manual cleanup. In this work, we present StableMotion, a simple yet effective method for training motion cleanup models directly from unpaired corrupted datasets that need cleanup. The core component of our method is the introduction of motion quality indicators, which can be easily annotated - through manual labeling or heuristic algorithms - and enable training of quality-aware motion generation models on raw motion data with mixed quality. At test time, the model can be prompted to generate high-quality motions using the quality indicators. Our method can be implemented through a simple diffusion-based framework, leading to a unified motion generate-discriminate model, which can be used to both identify and fix corrupted frames. We demonstrate that our proposed method is effective for training motion cleanup models on raw mocap data in production scenarios by applying StableMotion to SoccerMocap, a 245-hour soccer mocap dataset containing real-world motion artifacts. The trained model effectively corrects a wide range of motion artifacts, reducing motion pops and frozen frames by 68% and 81%, respectively. Results and code are available at https://yxmu.foo/stablemotion-page

cs.CV↗

arXiv2025

A Novel Quantum Fourier Ordinary Differential Equation Solver for Solving Linear and Nonlinear Partial Differential Equations

In this work, a novel quantum Fourier ordinary differential equation (ODE) solver is proposed to solve both linear and nonlinear partial differential equations (PDEs). Traditional quantum ODE solvers transform a PDE into an ODE system via spatial discretization and then integrate it, thereby converting the task of solving the PDE into computing the integral for the driving function $f(x)$. These solvers rely on the quantum amplitude estimation algorithm, which requires the driving function $f(x)$ to be within the range of [0, 1] and necessitates the construction of a quantum circuit for the oracle R that encodes $f(x)$. This construction can be highly complex, even for simple functions like $f(x) = x$. An important exception arises for the specific case of $f(x) = sin^2(mx+c)$, which can be encoded more efficiently using a set of $Ry$ rotation gates. To address these challenges, we expand the driving function $f(x)$ as a Fourier series and propose the Quantum Fourier ODE Solver. This approach not only simplifies the construction of the oracle R but also removes the restriction that $f(x)$ must lie within [0,1]. The proposed method was evaluated by solving several representative linear and nonlinear PDEs, including the Navier-Stokes (N-S) equations. The results show that the quantum Fourier ODE solver produces results that closely match both analytical and reference solutions.

quant-ph↗

arXiv2024

Non-Bloch Dirac Points and Phase Diagram in the Stacked Non-Hermitian SSH Model

Topological semimetals exhibit protected band crossings in momentum space, accompanied by corresponding surface states. Non-Hermitian Hamiltonians introduce geometry-sensitive features that dissolve this bulk-boundary correspondence principle. In this paper, we exemplify this phenomenon by investigating a non-Hermitian 2D stacked SSH chain model with non-reciprocal hopping and on-site gain/loss. We derive an analytical phase diagram in terms of the complex energy gaps in the open-boundary spectrum. The phase diagram reveals the existence of non-Bloch Dirac points, which feature a real spectrum and only appear under open boundary conditions but disappear in Bloch bands under periodic boundary conditions. Due to the reality of the spectrum in the vicinity of non-Bloch Dirac points, we can locally map it to Hermitian semimetals within the Altland-Zirnabuer symmetry classes. Based on this mapping, we demonstrate that non-Bloch Dirac points are characterized by an integer topological charge. Unlike the band crossings in Hermitian semimetals, the locations of the non-Bloch Dirac points under different boundary geometries do not match each other, indicating a geometry-dependent bulk-boundary correspondence in non-Hermitian semimetals. Our findings provide new pathways into establishing unconventional bulk-boundary correspondence for non-Bloch Dirac metals in non-Hermitian systems.

cond-mat.mes-hall↗

arXiv2024

Subassembly to Full Assembly: Effective Assembly Sequence Planning through Graph-based Reinforcement Learning

This paper proposes an assembly sequence planning framework, named Subassembly to Assembly (S2A). The framework is designed to enable a robotic manipulator to assemble multiple parts in a prespecified structure by leveraging object manipulation actions. The primary technical challenge lies in the exponentially increasing complexity of identifying a feasible assembly sequence as the number of parts grows. To address this, we introduce a graph-based reinforcement learning approach, where a graph attention network is trained using a delayed reward assignment strategy. In this strategy, rewards are assigned only when an assembly action contributes to the successful completion of the assembly task. We validate the framework's performance through physics-based simulations, comparing it against various baselines to emphasize the significance of the proposed reward assignment approach. Additionally, we demonstrate the feasibility of deploying our framework in a real-world robotic assembly scenario.

cs.RO↗

arXiv2024

Ultra spectral sensitivity and non-local bi-impurity bound states from quasi-long-range non-hermitian skin modes

A fundamental tenet of quantum mechanics is that the energy spectrum of a quantum system shall remain stable against infinitesimally weak and spatially confined perturbations. In this article, we demonstrate that this principle of spectral stability fails in non-Hermitian systems at the thermodynamic limit. Consider, for instance, a non-interacting non-Hermitian system $H_0$ with a couple of point-like impurities, each of which introduces a local short-range potential $V_i$ with $i=1, \ldots, n$ labeling the impurities. If the impurity potentials are sufficiently weak, introducing a single impurity will not alter the spectrum; that is, $H_0$ and $H_0 + V_1$ have nearly identical energy spectra. However, if a second impurity is introduced, $H_0 + V_1 + V_2$, we find that no matter how weak these local potentials are, as long as the distance between them is sufficiently large, significant alterations in the energy spectrum can arise, directly contradicting the traditional expectation of a stable spectrum. Remarkably, this phenomenon is non-local, and the impact of the perturbations increases exponentially with the distance between the two impurities. In other words, although the Hamiltonian is entirely local, its energy spectrum, which is blind to the presence of a single infinitesimally weak impurity, is capable of detecting the presence of two infinitesimally weak impurities separated by a large distance in space. Using Green's function techniques, we uncover the origin of this spectral sensitivity, which arises from the formation of non-local bi-impurity bound states: non-local stationary states with wavepackets propagating back-and-forth between the two impurities. We provide an analytic theory to identify and characterize such spectral instabilities, showing perfect agreement with numerical solutions.

cond-mat.mes-hall↗

arXiv2024

Algebraic non-Hermitian skin effect and generalized Fermi surface formula in arbitrary dimensions

The non-Hermitian skin effect, characterized by a proliferation of exponentially localized edge modes in open-boundary systems, has led to the discovery of numerous novel physical phenomena that challenge the limits of conventional band theory. In sharp contrast to this familiar exponential localization, we report a distinct phenomenon--the algebraic non-Hermitian skin effect--which arises generically in non-Hermitian systems with two or more spatial dimensions. In such cases, the amplitude of skin modes typically decays from the boundary following a power law, rather than an exponential form--a behavior not captured by existing theoretical frameworks. To bridge this gap and describe the transition in localization from one to higher dimensions, we develop a generalized Fermi surface framework applicable to open-boundary systems in arbitrary dimensions. This framework not only reproduces known results for the exponential skin effect in 1D, but also predicts a new class of skin effects with algebraic decay in 2D and above. We demonstrate this framework in both tight-binding and continuum models in two and three dimensions. This investigation not only unveils a novel category of the non-Hermitian skin effect but also offers a comprehensive theoretical structure that describes skin effects in any non-Hermitian system, irrespective of its spatial dimensionality.

cond-mat.mes-hall↗

arXiv2024

Loss-induced universal one-way transport in periodically driven systems

In this paper, we show that a periodically driven Aubry-André-Harper model with imbalanced on-site gain/loss supports universal one-way transport that is immune to impurities and independent of initial excitations. We reveal the underlying mechanism that the periodic driving gives rise to the non-Hermitian skin effect in the effective Floquet Hamiltonian, thereby causing universal non-reciprocal transport. Additionally, we probe the time-average decay rate of the propagator under long-time bulk dynamics as a signature of the Floquet emergent non-Hermitian skin effect. Our results provide a feasible and controllable way to realize universal one-way transport that is easily accessible to experiments.

quant-ph↗