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Xiao Chen

Publications and source records attributed to Xiao Chen.

At least 91 records · Page 5Linked to original sources
arXiv2025

FedOAED: Federated On-Device Autoencoder Denoiser for Heterogeneous Data under Limited Client Availability

Over the last few decades, machine learning (ML) and deep learning (DL) solutions have demonstrated their potential across many applications by leveraging large amounts of high-quality data. However, strict data-sharing regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) have prevented many data-driven applications from being realised. Federated Learning (FL), in which raw data never leaves local devices, has shown promise in overcoming these limitations. Although FL has grown rapidly in recent years, it still struggles with heterogeneity, which produces gradient noise, client-drift, and increased variance from partial client participation. In this paper, we propose FedOAED, a novel federated learning algorithm designed to mitigate client-drift arising from multiple local training updates and the variance induced by partial client participation. FedOAED incorporates an on-device autoencoder denoiser on the client side to mitigate client-drift and variance resulting from heterogeneous data under limited client availability. Experiments on multiple vision datasets under Non-IID settings demonstrate that FedOAED consistently outperforms state-of-the-art baselines.

cs.LG↗
arXiv2025

A Unified Framework for Optimizing Uniformly Controlled Structures in Quantum Circuits

Quantum unitaries of the form $Σ_{c}\ket{c}\bra{c}\otimes U_{c}$ are ubiquitous in quantum algorithms. This class encompasses not only standard uniformly controlled gates (UCGs) but also a wide range of circuits with uniformly controlled structures. However, their circuit-depth and gate-count complexities have not been systematically analyzed within a unified framework. In this work, we study the general decomposition problem for UCG and UCG-like structure. We then introduce the restricted Uniformly Controlled Gates (rUCGs) as a unified algebraic model, defined by a 2-divisible Abelian group that models the controlled gate set. This model captures uniformly controlled rotations, multi-qubit uniformly controlled gates, and diagonal unitaries. Furthermore, this model also naturally incorporates k-sparse version (k-rUCGs), where only a subset of control qubits participate in each multi-qubit gate. Building on this algebraic model, we develop a general framework. For an n-control rUCG, the framework reduce the gate complexity from ${O(n2^n)}$ to ${O(2^n})$ and the circuit depth from ${O(2^n\log n)}$ to ${O(2^n\log n/n)}$. The framework further provides systematic size and depth bounds for k-rUCGs by exploiting sparsity in the control space, with same optimization coefficient as rUCG, respectively. Empirical evaluations on representative QAOA circuits confirm reductions in depth and size, which highlight that the rUCG model and its associated decomposition framework unify circuits previously considered structurally distinct under a single, asymptotically optimal synthesis paradigm.

quant-ph↗
arXiv2025

Learning to Control Physically-simulated 3D Characters via Generating and Mimicking 2D Motions

Video data is more cost-effective than motion capture data for learning 3D character motion controllers, yet synthesizing realistic and diverse behaviors directly from videos remains challenging. Previous approaches typically rely on off-the-shelf motion reconstruction techniques to obtain 3D trajectories for physics-based imitation. These reconstruction methods struggle with generalizability, as they either require 3D training data (potentially scarce) or fail to produce physically plausible poses, hindering their application to challenging scenarios like human-object interaction (HOI) or non-human characters. We tackle this challenge by introducing Mimic2DM, a novel motion imitation framework that learns the control policy directly and solely from widely available 2D keypoint trajectories extracted from videos. By minimizing the reprojection error, we train a general single-view 2D motion tracking policy capable of following arbitrary 2D reference motions in physics simulation, using only 2D motion data. The policy, when trained on diverse 2D motions captured from different or slightly different viewpoints, can further acquire 3D motion tracking capabilities by aggregating multiple views. Moreover, we develop a transformer-based autoregressive 2D motion generator and integrate it into a hierarchical control framework, where the generator produces high-quality 2D reference trajectories to guide the tracking policy. We show that the proposed approach is versatile and can effectively learn to synthesize physically plausible and diverse motions across a range of domains, including dancing, soccer dribbling, and animal movements, without any reliance on explicit 3D motion data. Project Website: https://jiann-li.github.io/mimic2dm/

cs.GR↗
arXiv2025

DynamicTree: Interactive Real Tree Animation via Sparse Voxel Spectrum

Generating dynamic and interactive 3D trees has wide applications in virtual reality, games, and world simulation. However, existing methods still face various challenges in generating structurally consistent and realistic 4D motion for complex real trees. In this paper, we propose DynamicTree, the first framework that can generate long-term, interactive 3D motion for 3DGS reconstructions of real trees. Unlike prior optimization-based methods, our approach generates dynamics in a fast feed-forward manner. The key success of our approach is the use of a compact sparse voxel spectrum to represent the tree movement. Given a 3D tree from Gaussian Splatting reconstruction, our pipeline first generates mesh motion using the sparse voxel spectrum and then binds Gaussians to deform the mesh. Additionally, the proposed sparse voxel spectrum can also serve as a basis for fast modal analysis under external forces, allowing real-time interactive responses. To train our model, we also introduce 4DTree, the first large-scale synthetic 4D tree dataset containing 8,786 animated tree meshes with 100-frame motion sequences. Extensive experiments demonstrate that our method achieves realistic and responsive tree animations, significantly outperforming existing approaches in both visual quality and computational efficiency.

cs.CV↗
arXiv2025

Continuous-time Discrete-space Diffusion Model for Recommendation

In the era of information explosion, Recommender Systems (RS) are essential for alleviating information overload and providing personalized user experiences. Recent advances in diffusion-based generative recommenders have shown promise in capturing the dynamic nature of user preferences. These approaches explore a broader range of user interests by progressively perturbing the distribution of user-item interactions and recovering potential preferences from noise, enabling nuanced behavioral understanding. However, existing diffusion-based approaches predominantly operate in continuous space through encoded graph-based historical interactions, which may compromise potential information loss and suffer from computational inefficiency. As such, we propose CDRec, a novel Continuous-time Discrete-space Diffusion Recommendation framework, which models user behavior patterns through discrete diffusion on historical interactions over continuous time. The discrete diffusion algorithm operates via discrete element operations (e.g., masking) while incorporating domain knowledge through transition matrices, producing more meaningful diffusion trajectories. Furthermore, the continuous-time formulation enables flexible adaptive sampling. To better adapt discrete diffusion models to recommendations, CDRec introduces: (1) a novel popularity-aware noise schedule that generates semantically meaningful diffusion trajectories, and (2) an efficient training framework combining consistency parameterization for fast sampling and a contrastive learning objective guided by multi-hop collaborative signals for personalized recommendation. Extensive experiments on real-world datasets demonstrate CDRec's superior performance in both recommendation accuracy and computational efficiency.

cs.IR↗
arXiv2025

MoETTA: Test-Time Adaptation Under Mixed Distribution Shifts with MoE-LayerNorm

Test-Time adaptation (TTA) has proven effective in mitigating performance drops under single-domain distribution shifts by updating model parameters during inference. However, real-world deployments often involve mixed distribution shifts, where test samples are affected by diverse and potentially conflicting domain factors, posing significant challenges even for SOTA TTA methods. A key limitation in existing approaches is their reliance on a unified adaptation path, which fails to account for the fact that optimal gradient directions can vary significantly across different domains. Moreover, current benchmarks focus only on synthetic or homogeneous shifts, failing to capture the complexity of real-world heterogeneous mixed distribution shifts. To address this, we propose MoETTA, a novel entropy-based TTA framework that integrates the Mixture-of-Experts (MoE) architecture. Rather than enforcing a single parameter update rule for all test samples, MoETTA introduces a set of structurally decoupled experts, enabling adaptation along diverse gradient directions. This design allows the model to better accommodate heterogeneous shifts through flexible and disentangled parameter updates. To simulate realistic deployment conditions, we introduce two new benchmarks: potpourri and potpourri+. While classical settings focus solely on synthetic corruptions, potpourri encompasses a broader range of domain shifts--including natural, artistic, and adversarial distortions--capturing more realistic deployment challenges. Additionally, potpourri+ further includes source-domain samples to evaluate robustness against catastrophic forgetting. Extensive experiments across three mixed distribution shifts settings show that MoETTA consistently outperforms strong baselines, establishing SOTA performance and highlighting the benefit of modeling multiple adaptation directions via expert-level diversity.

cs.LG↗
arXiv2025

From One to More: Contextual Part Latents for 3D Generation

Recent advances in 3D generation have transitioned from multi-view 2D rendering approaches to 3D-native latent diffusion frameworks that exploit geometric priors in ground truth data. Despite progress, three key limitations persist: (1) Single-latent representations fail to capture complex multi-part geometries, causing detail degradation; (2) Holistic latent coding neglects part independence and interrelationships critical for compositional design; (3) Global conditioning mechanisms lack fine-grained controllability. Inspired by human 3D design workflows, we propose CoPart - a part-aware diffusion framework that decomposes 3D objects into contextual part latents for coherent multi-part generation. This paradigm offers three advantages: i) Reduces encoding complexity through part decomposition; ii) Enables explicit part relationship modeling; iii) Supports part-level conditioning. We further develop a mutual guidance strategy to fine-tune pre-trained diffusion models for joint part latent denoising, ensuring both geometric coherence and foundation model priors. To enable large-scale training, we construct Partverse - a novel 3D part dataset derived from Objaverse through automated mesh segmentation and human-verified annotations. Extensive experiments demonstrate CoPart's superior capabilities in part-level editing, articulated object generation, and scene composition with unprecedented controllability.

cs.CV↗
arXiv2025

FullPart: Generating each 3D Part at Full Resolution

Part-based 3D generation holds great potential for various applications. Previous part generators that represent parts using implicit vector-set tokens often suffer from insufficient geometric details. Another line of work adopts an explicit voxel representation but shares a global voxel grid among all parts; this often causes small parts to occupy too few voxels, leading to degraded quality. In this paper, we propose FullPart, a novel framework that combines both implicit and explicit paradigms. It first derives the bounding box layout through an implicit box vector-set diffusion process, a task that implicit diffusion handles effectively since box tokens contain little geometric detail. Then, it generates detailed parts, each within its own fixed full-resolution voxel grid. Instead of sharing a global low-resolution space, each part in our method - even small ones - is generated at full resolution, enabling the synthesis of intricate details. We further introduce a center-point encoding strategy to address the misalignment issue when exchanging information between parts of different actual sizes, thereby maintaining global coherence. Moreover, to tackle the scarcity of reliable part data, we present PartVerse-XL, the largest human-annotated 3D part dataset to date with 40K objects and 320K parts. Extensive experiments demonstrate that FullPart achieves state-of-the-art results in 3D part generation. We will release all code, data, and model to benefit future research in 3D part generation.

cs.CV↗
arXiv2025

Towards Adaptable Humanoid Control via Adaptive Motion Tracking

Humanoid robots are envisioned to adapt demonstrated motions to diverse real-world conditions while accurately preserving motion patterns. Existing motion prior approaches enable well adaptability with a few motions but often sacrifice imitation accuracy, whereas motion-tracking methods achieve accurate imitation yet require many training motions and a test-time target motion to adapt. To combine their strengths, we introduce AdaMimic, a novel motion tracking algorithm that enables adaptable humanoid control from a single reference motion. To reduce data dependence while ensuring adaptability, our method first creates an augmented dataset by sparsifying the single reference motion into keyframes and applying light editing with minimal physical assumptions. A policy is then initialized by tracking these sparse keyframes to generate dense intermediate motions, and adapters are subsequently trained to adjust tracking speed and refine low-level actions based on the adjustment, enabling flexible time warping that further improves imitation accuracy and adaptability. We validate these significant improvements in our approach in both simulation and the real-world Unitree G1 humanoid robot in multiple tasks across a wide range of adaptation conditions. Videos and code are available at https://taohuang13.github.io/adamimic.github.io/.

cs.RO↗
arXiv2025

Dominant spin-spin relaxation mechanism at clock transition of the $Ho_{x}Y_{1-x}W_{10}$ complex at different concentrations

Spin decoherence poses a significant challenge in molecular magnets, with the nuclear spin bath serving as a prominent source. Intriguingly, spin qubits at the clock transition exhibit remarkable insensitivity to the surrounding nuclear spins. Recent experimental studies have unveiled a correlation between the decoherence time and the density of spin qubits, prompting our investigation into the contribution of the qubit bath to spin decoherence. In this paper, we present a comprehensive theoretical analysis of a few S=1 spin qubits, focusing on their interaction at the clock transition. Employing the exact diagonalization and the cluster correlation expansion (CCE) method, we simulate the dynamics of spin decoherence while varying the density of the qubit bath. To ensure the realism of our simulations, we incorporate structural and energetic parameters derived from previous studies on the HoW10 crystal. Our findings indicate that when the energy mismatch between the energy splittings of two qubits exceeds their interaction strength, they can become effectively insensitive to each other, offering an explanation for the absence of observed changes in the T2 time during experiments with lower qubit densities. Understanding the role of qubit bath density in spin decoherence at the clock transition not only advances our knowledge of decoherence mechanisms but also provides insights for the development of strategies to protect coherence in molecular magnets and other quantum systems. By optimizing the density of spin qubits, we can enhance the coherence properties and pave the way for improved performance of quantum devices. Overall, this study offers valuable insights into the relationship between qubit bath density and spin decoherence at the clock transition, contributing to the broader understanding and control of quantum systems in molecular magnets.

quant-ph↗
arXiv2025

PhysHSI: Towards a Real-World Generalizable and Natural Humanoid-Scene Interaction System

Deploying humanoid robots to interact with real-world environments--such as carrying objects or sitting on chairs--requires generalizable, lifelike motions and robust scene perception. Although prior approaches have advanced each capability individually, combining them in a unified system is still an ongoing challenge. In this work, we present a physical-world humanoid-scene interaction system, PhysHSI, that enables humanoids to autonomously perform diverse interaction tasks while maintaining natural and lifelike behaviors. PhysHSI comprises a simulation training pipeline and a real-world deployment system. In simulation, we adopt adversarial motion prior-based policy learning to imitate natural humanoid-scene interaction data across diverse scenarios, achieving both generalization and lifelike behaviors. For real-world deployment, we introduce a coarse-to-fine object localization module that combines LiDAR and camera inputs to provide continuous and robust scene perception. We validate PhysHSI on four representative interactive tasks--box carrying, sitting, lying, and standing up--in both simulation and real-world settings, demonstrating consistently high success rates, strong generalization across diverse task goals, and natural motion patterns.

cs.RO↗
arXiv2025

Comprehensive X-ray Spectral-timing Analysis of GRS 1915+105 Based on Insight-HXMT Observations

GRS 1915+105 has been well studied since its discovery, and is well-known for its complex light curve variability. Using the full currently available Insight-HXMT dataset from July 2017 to June 2023, we make a comprehensive spectral-timing analysis of this source and report four main findings. First, we uncover a QPO frequency rising branch between MJD 58206 and 58230, where the centroid frequency increases from $\sim$2 Hz to $\sim$6 Hz, consistent with a spectral state transition from the hard to intermediate state. This rising branch completes the full QPO frequency evolution cycle when combined with the subsequent frequency decay phase, and had been missed in prior NICER and Insight-HXMT studies. Second, we identify a previously unreported Flare 3 during the obscured state, which shows distinct spectral and timing properties compared to the earlier flares. Third, we detect sub-Hz QPOs (<1 Hz) in all three flares, specifically at $\sim$0.01 Hz in Flare 1 and $\sim$0.2 Hz in both Flares 2 and 3. In particular, the weak $\sim$0.2 Hz signals observed in Flare 3 indicate ongoing coronal activity despite strong obscuration. Finally, a comparison between QPOs above and below 1 Hz suggests distinct origins, with the former likely arising from Lense-Thirring precession of the inner hot flow and the latter from magnetic perturbations driving a failed disk wind. These findings offer new insights into the unique accretion geometry and variability behaviors of GRS 1915+105.

astro-ph.HE↗
arXiv2025

Broad nonlocal spectrum in the Pb-InSb hybrid three terminals for potential realization of Kitaev chains

Hybrid superconductor-semiconductor(SC-SM) nanowires remain one of the foremost platforms for engineering topological superconductivity and Majorana zero modes(MZMs) towards fault-tolerant topological qubits, especially with the rapid development of artificial Kitaev chains. In contrast to the widely used aluminum(Al)-based hybrids, lead(Pb) offers a bulk superconducting gap of ~1.4meV and a critical temperature of ~7.2K, giving rise to a proximity-induced gap that is roughly five times larger than that obtained with Al. Here we present the first three-terminal Pb-hybrid devices and perform nonlocal differential-conductance spectroscopy on this platform. The nonlocal measurement simultaneously resolves a dual-gap feature of the parent Pb gap and the large, hard, gate-tunable induced superconducting gap, distinguished by a switch between electron- and hole-like dissipation processes. Within the induced gap we observe several types of Andreev bound states(ABSs) that undergo singlet-doublet transitions. Moreover, by tuning gate voltages we achieve gate-controlled resonating sign reversals of the nonlocal conductance, identifying three distinct regimes that correspond to different configurations of quantum-dot(QD) resonances(single-resonance, double-resonance, and series-resonance). Finally, the coupling between ABSs and QDs also present and can be modulated from the weak- to strong-coupling limit, indicating the feasibility of realizing the artificial Kitaev chains. Crucially, the robust nonlocal signatures persist up to temperatures(~1K) far above the operating temperature of Al-based devices thanks to the unusually large induced gap, thereby widening the accessible parameter space greatly and underscoring the suitability of Pb-based hybrids for implementing warm temperature artificial Kitaev chains and the topological quantum devices protected by a substantially larger topological gap.

quant-ph↗
arXiv2025

From Quantum Circuits with Ultraslow Dynamics to Classical Plaquette Models

We introduce a family of hybrid quantum circuits involving unitary gates and projective measurements that display a measurement-induced phase transition. Remarkably, the volume-law phase featuring logarithmic entanglement growth for certain initial states. We attribute this slow entanglement growth to the similarly slow growth of the participation entropy, which bounds the entanglement. Furthermore, the quantum circuit can be mapped to a classical spin model with real positive Boltzmann weights which involves local multi-spin interactions and displays glassy dynamics at finite temperature. We trace the origin of both the slow quantum dynamics and the classical glassiness to the presence of large, non-local symmetry operators. Our work establishes a novel connection between quantum entanglement dynamics and classical glassy behavior, offering a new geometric perspective on entanglement phase transitions.

quant-ph↗
arXiv2025

Tele-rehabilitation with online skill transfer and adaptation in $\mathbb{R}^3 \times \mathit{S}^3$

This paper proposes a tele-teaching framework for the domain of robot-assisted tele-rehabilitation. The system connects two robotic manipulators on therapist and patient side via bilateral teleoperation, enabling a therapist to remotely demonstrate rehabilitation exercises that are executed by the patient-side robot. A 6-DoF Dynamical Movement Primitives formulation is employed to jointly encode translational and rotational motions in $\mathbb{R}^3 \times \mathit{S}^3$ space, ensuring accurate trajectory reproduction. The framework supports smooth transitions between therapist-led guidance and patient passive training, while allowing adaptive adjustment of motion. Experiments with 7-DoF manipulators demonstrate the feasibility of the approach, highlighting its potential for personalized and remotely supervised rehabilitation.

cs.RO↗
arXiv2025

GLEAM: Learning Generalizable Exploration Policy for Active Mapping in Complex 3D Indoor Scenes

Generalizable active mapping in complex unknown environments remains a critical challenge for mobile robots. Existing methods, constrained by insufficient training data and conservative exploration strategies, exhibit limited generalizability across scenes with diverse layouts and complex connectivity. To enable scalable training and reliable evaluation, we introduce GLEAM-Bench, the first large-scale benchmark designed for generalizable active mapping with 1,152 diverse 3D scenes from synthetic and real-scan datasets. Building upon this foundation, we propose GLEAM, a unified generalizable exploration policy for active mapping. Its superior generalizability comes mainly from our semantic representations, long-term navigable goals, and randomized strategies. It significantly outperforms state-of-the-art methods, achieving 66.50% coverage (+9.49%) with efficient trajectories and improved mapping accuracy on 128 unseen complex scenes. Project page: https://xiao-chen.tech/gleam/.

cs.CV↗
arXiv2025

Accurate crystal field Hamiltonians of single-ion magnets at mean-field cost

The effective crystal field Hamiltonian provides the key description of the electronic properties of single-ion magnets, but obtaining its parameters from ab initio computation is challenging. We introduce a simple approach to derive the effective crystal field Hamiltonian through density functional calculations of randomly rotated mean-field states within the low-energy manifold. In benchmarks on five lanthanide-based complexes, we find that we compute with mean-field cost an effective crystal field Hamiltonian that matches the state-of-the-art from much more expensive multi-configurational quantum chemistry methods. In addition, we are able to reproduce the experimental low-energy spectrum and magnetic properties with an accuracy exceeding prior attempts. Due to its low cost, our approach provides a crucial ingredient in the computational design of single-ion magnets with tailored physical properties and low-energy spectra.

physics.chem-ph↗