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Jun Hu

Publications and source records attributed to Jun Hu.

At least 37 records · Page 2Linked to original sources
arXiv2026

Generalized Hyperbolic Conical Circle Packings associated with Finite Polygonal Decompositions of Surfaces with Boundary

Let S be a compact topological surface with finitely many genus and finitely many holes and let D be a polygonal decomposition of S. In this paper, we consider the generalized hyperbolic conical circle packings associated with D. We first show that the boundary value problem has a unique solution k by prescribing total geodesic curvatures of generalized hyperbolic conical circles centered at interior vertices and geodesic curvatures of generalized hyperbolic conical circles centered at boundary vertices. Then we show that such a solution k can be obtained by taking a limit of the packings inductively modified by Thurston's algorithm via an arbitrarily chosen initial generalized hyperbolic conical circle packing associated with D and with given boundary values. Thirdly, we develop the so-called discrete Schwarz-Pick lemma for the solution packing k on D.

math.GT↗
arXiv2026

Random Reshuffling-Based Distributed Nash Equilibrium Seeking

This paper studies random reshuffling (RR)-based distributed Nash equilibrium seeking for noncooperative games. The game is motivated as a sample-average approximation of an underlying expected-value stochastic game, while the algorithmic focus is placed on the resulting finite-sum equilibrium problem. Unlike existing distributed stochastic Nash equilibrium methods that mainly rely on with-replacement sampling, the proposed approach incorporates without-replacement component updates into equilibrium computation over networks. We first consider a full-information benchmark, for which an intermediate reference trajectory and a shuffling variance are introduced to characterize the epoch-wise dynamics induced by RR. The method is then extended to the more practical partial-decision-information setting, where each player updates its action using local estimates of the joint action profile. For the full-information case, a descent-type bound is established for the RR iterates. For the distributed partial-decision-information case, it is shown that, under constant parameters, the proposed algorithm converges linearly to a neighborhood of the Nash equilibrium, while under diminishing parameters, it converges exactly to the Nash equilibrium almost surely and in mean square. Numerical experiments on an EV charging game and a nonquadratic edge resource admission game demonstrate that RR consistently outperforms the conventional with-replacement SGD baseline in both steady-state accuracy and long-horizon performance.

math.OC↗
arXiv2026

A cryogenic gas target for high-intensity radioactive ion beam production at HIRFL-RIBLL

A liquid-nitrogen-cooled cryogenic gas target system has been developed and installed for radioactive ion beam (RIB) production at the Radioactive Ion Beam Line in Lanzhou (RIBLL). Light-element gases ($\mathrm{H}_2$, $\mathrm{D}_2$, and $^4\mathrm{He}$) filled in the target cell were cooled to cryogenic temperatures, with the gas-cell outlet temperature typically monitored at 82--86 K during beam irradiation and operating pressures up to 1000 mbar. The system was used to produce $^{7}\mathrm{Be}$, $^{16}\mathrm{N}$, and $^{15}\mathrm{O}$ RIBs via the $^{1}\mathrm{H}(^{7}\mathrm{Li}, ^{7}\mathrm{Be})n$, $^{2}\mathrm{H}(^{15}\mathrm{N}, ^{16}\mathrm{N})p$, and $^{1}\mathrm{H}(^{15}\mathrm{N}, ^{15}\mathrm{O})n$ inverse kinematics reactions, yielding purities of 85\%, 99\%, and 95\%, with intensities of $1.02\times10^{6}$, $2.7\times10^{5}$, and $1.0\times10^{5}$ pps, respectively. A $^{93m}\mathrm{Mo}$ isomer beam was also produced via the $\mathrm{^4He(^{94}Zr,} 5n)^{93m}\mathrm{Mo}$ reaction, achieving an intensity of $5.38\times10^{3}$ pps and a purity of 20\% (which can be further improved to $\sim$50\% with offline time-of-flight gating). By delivering a broader range of high-intensity secondary RIBs, this setup establishes a robust platform at RIBLL for low- and medium-energy nuclear astrophysics and reaction studies.

physics.ins-det↗
arXiv2026

Test of the JUNO 20-inch PMTs during Installation

Photomultiplier tubes (PMTs) are widely used in neutrino experiments. As a new-generation neutrino observatory, JUNO requires an excellent energy resolution of 3% at 1 MeV. This will be realized with a 20 kton liquid scintillator detector instrumented with more than 20000 20-inch PMTs and 25600 3-inch PMTs. These PMTs were successfully installed in JUNO from October 2022 to December 2024. During the installation, seven test campaigns were performed to validate the PMT functionality, including measurements dark count rate, gain, waveform, and charge spectrum. In this paper, we present the implementation of these tests and the corresponding results for the 20-inch PMTs throughout the installation process.

physics.ins-det↗
arXiv2026

Folded Transport MCMC: Eliminating Label Switching by Sampling on a Fundamental Domain

In Bayesian mixture models and other exchangeable-component models, the posterior is invariant under permutation of component labels, creating m! equivalent modes-the label-switching problem. Standard MCMC methods either mix poorly across these modes or rely on post-hoc relabelling that cannot guarantee the sampler has converged. We propose Folded Transport MCMC (FolT-MCMC), which eliminates label switching before sampling by restricting the Markov chain to a fundamental domain-a sorted or reflected subspace containing exactly one representative from each symmetric mode. The proposal is a learned normalising flow whose density is symmetrised over the group orbits, ensuring correct targeting on the reduced space. We show that this construction preserves a computable convergence diagnostic based on the oscillation of the log-density ratio, and that the diagnostic becomes sharper on the fundamental domain whenever the original-space flow under-covers one or more symmetric modes. Experiments on Gaussian mixtures (d=2-20), label-switching targets (up to 24 equivalent modes), a standard Bayesian three-component mixture posterior, and real accelerometer data from a supertall building show improvement ratios of 2x to 145x, with the folded diagnostic stable across dimensions while the unfolded diagnostic collapses.

cs.LG↗
arXiv2026

Integrated Terahertz Photonic Receiving Frontend with Link Noise Outperforming Electronics

Terahertz technology is a key enabler for sixth-generation (6G) wireless networks, yet its application is constrained by increasingly severe free-space loss at high frequencies. To efficiently retrieve weak signals at the receiving end, a compact frontend that features both a high-gain antenna and a low-noise signal-detection chain is critical. Current transistor-based THz electronic frontends face significant challenges in meeting these demands because both on-chip antenna efficiency and transistor noise performance degrade rapidly when approaching their cut-off frequencies. Photonic technology provides an alternative solution to circumvent the transistor bandwidth limit, yet most microwave photonic links to date exhibit noise performance substantially worse than state-of-the-art electronics. Here, we demonstrate low-noise integrated THz photonic frontends that deliver undegraded link noise performance across three major THz windows from 140 to 450 GHz, and outperform electronic frontends in the upper two windows. We achieve this through co-design of high-gain on-chip THz antenna array and broadband THz-optic modulator on a single thin-film lithium niobate (TFLN) chip, leading to distributed reception of free-space THz signals and continuous coherent build-up of the THz-optic conversion process with unprecedented efficiency. Combined with an efficient heterodyne detection chain, our integrated frontends exhibit effective isotropic noise figures of 13.6 and 16.2 dB at 250 and 450 GHz, respectively, both setting new benchmarks in their respective bands. We further demonstrate 6G-oriented multi-link communication up to 20 Git/s. Our integrated frontends represent a significant step towards compact, cost-effective and energy-efficient THz wireless systems in 6G and beyond.

physics.optics↗
arXiv2026

A High-Precision Clock Synchronization System for the CEPC Accelerator

The Circular Electron Positron Collider (CEPC) distributes a reference clock distributed to 192 control nodes along its 100~km underground tunnel. The required synchronization precision is 30~ps (standard deviation). We present an enhanced White Rabbit (WR)-based clock synchronization system designed to meet this requirement. A noise-budget analysis of the standard WR slave loop identifies the analog actuation chain (DAC + VCXO + multiplier PLL) and restart-induced timing uncertainty as the dominant limitations. In our redesigned node, the DAC+VCXO chain is replaced by a Si5345A DSPLL clock generator with DCO-based phase control, removing the board-level analog tuning stage. GTX transceiver phase alignment and manual byte-alignment fixing reduce restart uncertainty from 88.8~ps to 12~ps peak-to-peak. For multi-node operation, we introduce a cascaded global-control architecture with PC-side PID auto-tuned by TD3 reinforcement learning, on-chip-temperature feed-forward calibrated to $-0.76\,\mathrm{ps}/^\circ\mathrm{C}$. The measured point-to-point synchronization precision is 3.38~ps over 1~m fiber and 3.92~ps over 50~km. In a 12-level cascade, the end-node precision reaches 6.66~ps at constant temperature and 7.30~ps under a 13$\,^\circ$C temperature swing. Synchronized-clock TIE jitter stays below 1~ps regardless of cascade depth. Restart uncertainty is 2.82~ps (std.\ dev.). A 4-level cascade operated stably for 25 hours of continuous monitoring. All measured metrics fall well within the CEPC 30~ps budget.

hep-ex↗
arXiv2026

Self-Certifying Transport MCMC via Dual Spectral-Gap Certificates

We propose CerT-MCMC, a framework that equips learned-transport Markov chain Monte Carlo with automatic, rigorous convergence certificates. A normalising flow maps a Gaussian reference to an approximation of the target posterior; the same flow then serves as both the independence Metropolis-Hastings proposal and the basis for a computable spectral-gap bound. We develop two complementary certificates. The covering certificate bounds the weight-ratio oscillation over the full proposal support via finite-sample covering arguments, yielding full-support spectral-gap bounds when a conservative gradient bound is available; its correction term scales as O(n^{-1/D}), making it rapidly weak and eventually vacuous as dimension increases. We prove a matching Omega(n^{-1/D}) lower bound, establishing that this barrier is intrinsic to pointwise Lipschitz certification. The quantile-core certificate restricts attention to a high-probability residual core on which the oscillation is controlled by one-dimensional empirical quantiles, with a finite-sample probability slack of O(n^{-1/2}), independent of the ambient dimension. On synthetic targets (D=2-20), structural-engineering posteriors (D=6,8), real-data logistic regression on the Heart Disease data set (D=13), and synthetic Bayesian logistic regression (D=20), the quantile-core certificate delivers non-vacuous spectral-gap bounds where the covering certificate is vacuous, and its spectral-gap proxy tracks empirical effective sample sizes within 7%. A negative control experiment confirms that the certificate discriminates flow quality by a factor exceeding 10x, whereas acceptance rates differ by only 1.15x. To our knowledge, the dual-certificate framework is the first to provide automatic, dimension-aware convergence certificates for learned-transport MCMC, distinguishing genuine transport failure from proof-technique limitations.

cs.LG↗
arXiv2026

Non-Vacuous Certification of Transport MCMC via Oscillation-Controlled Normalizing Flows

Transport MCMC trains a normalizing flow to precondition Metropolis--Hastings proposals, achieving high empirical efficiency on challenging posteriors; yet no prior work produces a numerically non-vacuous, rigorous spectral-gap bound for such samplers. We establish the first such bounds. For independence MH on the banana family we certify (γ^\ast = 0.828) at (D = 2) (covering in the original space) and (γ^\ast \ge 7.6\times 10^{-4}) at (D = 5) (covering in an analytically unwarped Gaussian space with a grid-certified gradient bound under the stated numerical Lipschitz certification), both rigorous at 95% confidence. The framework rests on three pillars: (i) spectral normalization with reduced scale clips constrains the flow Lipschitz constant from (10^{47}) to (10^4); (ii) a coverage-based empirical oscillation bound replaces the vacuous analytical bound with a data-dependent certificate; and (iii) oscillation-regularised training cuts the empirical oscillation by 60--90% at no cost to density fit, extending practical certificates through (D = 20) ((γ^\ast \ge 1.7\times 10^{-4})). Tests on four further targets (Gaussian mixture, shear-building, Neal's funnel, Bayesian logistic regression) identify three precise barriers: boundary curvature, target stiffness, and tail-coverage mismatch. An affine-vs-spline comparison shows that simpler architectures yield tighter certificates at identical NLL, inverting the usual expressiveness hierarchy.

cs.LG↗
arXiv2026

World Models for Robotic Manipulation: A Survey

Robotic manipulation depends on the ability to anticipate how actions reshape objects, contacts, and scene geometry before execution. Learned world models provide this capability by predicting task-relevant future evolution under robot intervention, yet the term now spans latent dynamics models, action-conditioned video generators, three- and four-dimensional scene predictors, physics-informed simulators, and predictive modules inside vision-language-action systems. This breadth has fragmented the literature and obscured the design choices that matter for manipulation. We survey world models for robotic manipulation through three questions: what future representation is predicted, how prediction is connected to action, and when prediction is used in the robot-learning pipeline. We operationally define a world model as an action-conditioned predictive system and distinguish it from perception modules, inverse models, policies, rewards, and value functions. We then organize existing work into five representation families, develop a functional taxonomy that separates integrated prediction-action models from explicit predictive planners, and characterize infrastructure roles including synthetic experience generation, candidate filtering, search-based evaluation, learned environments, and outcome verification. We further map these roles across pretraining, post-training, and inference adaptation, review 34 manipulation datasets, and synthesize evaluation protocols for predictive fidelity, task performance, and simulator reliability. This survey shows that world models are evolving from task-specific dynamics predictors into predictive infrastructure for robot learning, while exposing open challenges in contact modeling, hallucination control, action alignment, and benchmarking under closed-loop use.

cs.RO↗
arXiv2026

Robust Multimodal Recommendation via Graph Retrieval-Enhanced Modality Completion

Multimodal data plays a critical role in web-based recommendation systems, where information from diverse modalities such as vision and text enhances representation learning. However, real-world multimodal datasets often suffer from modality incompleteness due to sensor failures, annotation scarcity, or privacy constraints, which substantially degrade model performance and reliability. One effective solution to address this issue is modality completion, which reconstructs missing features to provide modality-complete graphs for downstream tasks. Given a query node with missing multimodal features, existing modality completion methods typically infer information from the node itself or its neighbors to reconstruct the missing modality. However, these methods may overlook semantically relevant context in the graph, which contains valuable cues that are non-trivial to capture through simple methods like neighborhood aggregation. In this work, we propose GRE-MC, a Graph Retrieval-Enhanced Modality Completion framework, to overcome these limitations. By introducing a modality-aware subgraph retrieval mechanism, GRE-MC selects semantically relevant subgraphs from the entire graph, providing richer contextual information for completing missing modalities. Subsequently, a graph transformer jointly encodes the query node and the retrieved subgraph via global attention to complete the missing features, while a learnable sparse-routing codebook regularizes latent embeddings into compact bases for improved robustness. Extensive experiments on multimodal recommendation benchmarks demonstrate that GRE-MC consistently outperforms state-of-the-art methods, validating the effectiveness of subgraph retrieval and joint-encoding graph transformer for robust modality completion.

cs.IR↗
arXiv2026

A Coupling Method of Mixed and Lagrange Finite Elements for Linear Elasticity Problem

This paper proposes a finite element method that couples mixed and Lagrange finite elements to efficiently capture stress concentrations in elasticity problems. The method employs conforming mixed finite elements in regions with stress concentration, while standard Lagrange elements are used elsewhere, achieving a balance between stress accuracy and computational efficiency. The well-posedness of the coupled formulation and optimal a priori error estimates are established, even when the size of the mixed finite element subregion is $O(h)$. Numerical experiments are presented to verify the theoretical convergence rates and to demonstrate the effectiveness and efficiency of the proposed method.

math.NA↗
arXiv2026

On the cocenter of the cyclotomic Hecke algebra of type $G(r,1,n)$

In this paper, we construct some integral bases for the cocenter of the cyclotomic Hecke algebra $\mathscr{H}_{n,K}$ of type $G(r,1,n)$ by generalizing Geck and Pfeiffer's work on the cocenters of the Iwahori-Hecke algebras associated to finite Weyl groups. We show that the dimensions of both the cocenter and the center of the cyclotomic Hecke algebra $\mathscr{H}_{n,K}$ are independent of the characteristic of the ground field, its Hecke parameter and cyclotomic parameters. As applications, we verify Chavli-Pfeiffer's conjecture on the polynomial coefficient $g_{w,C}$ for the complex reflection group of type $G(r,1,n)$ and also show that both the cocenters and the centers of certain cyclotomic KLR algebras of affine type $A$ are independent of the characteristic of the ground field.

math.RT↗
arXiv2026

Spin-biased quantum spin Hall effect in altermagnetic Lieb lattice

Altermagnetic (AM) order, a recently discovered magnetic state, has attracted intense research interest for its potential applications in spintronic and quantum technologies. Here, we theoretically investigate the AM state in the Lieb lattice, a prototypical two-dimensional lattice, using the Hubbard model. We show that AM order emerges with only moderate electronic correlations. Strikingly, spin-orbit coupling drives the system into a topological phase exhibiting a new quantum spin Hall effect (QSHE) with spin-biased topological edge states in one-dimensional nanoribbons. These edge states possess different localizations and velocities, and hence may produce spin and charge currents, fundamentally distinct from that in conventional topological insulators with spin degeneracy. This novel spin-biased QSHE in the AM Lieb lattice unveils exciting opportunities for both fundamental studies and innovative device concepts, motivating immediate experimental exploration.

cond-mat.mes-hall↗
arXiv2026

Perron's method and spherical ideal circle patterns with prescribed total geodesic curvatures

In this paper, we apply the classical Perron method to give a proof of the existence and uniqueness/rigidity result of a circle pattern on a closed surface equipped with conical spherical metric when prescribed measures of the angles of intersecting circles stay in the range (0,π/2] and total geodesic curvatures are assigned to the circles, which is recently obtained in [3] via Colin de Verdière's variation method. Then we show the convergence of Thurston's algorithm, which adjusts the geodesic curvatures of circles one by one based on the prescribed values for total geodesic curvatures of the circles, to the desired circle pattern in the setting of the result.

math.GT↗
arXiv2026

Consistent and Efficient MSCKF-based LiDAR-Inertial Odometry with Inferred Cluster-to-Plane Constraints for UAVs

Robust and accurate navigation is critical for Unmanned Aerial Vehicles (UAVs) especially for those with stringent Size, Weight, and Power (SWaP) constraints. However, most state-of-the-art (SOTA) LiDAR-Inertial Odometry (LIO) systems still suffer from estimation inconsistency and computational bottlenecks when deployed on such platforms. To address these issues, this paper proposes a consistent and efficient tightly-coupled LIO framework tailored for UAVs. Within the efficient Multi-State Constraint Kalman Filter (MSCKF) framework, we build coplanar constraints inferred from planar features observed across a sliding window. By applying null-space projection to sliding-window coplanar constraints, we eliminate the direct dependency on feature parameters in the state vector, thereby mitigating overconfidence and improving consistency. More importantly, to further boost the efficiency, we introduce a parallel voxel-based data association and a novel compact cluster-to-plane measurement model. This compact measurement model losslessly reduces observation dimensionality and significantly accelerating the update process. Extensive evaluations demonstrate that our method outperforms most state-of-the-art (SOTA) approaches by providing a superior balance of consistency and efficiency. It exhibits improved robustness in degenerate scenarios, achieves the lowest memory usage via its map-free nature, and runs in real-time on resource-constrained embedded platforms (e.g., NVIDIA Jetson TX2).

cs.RO↗
arXiv2026

Effective and Efficient Masked Image Generation Models

Although masked image generation models and masked diffusion models are designed with different motivations and objectives, we observe that they can be unified within a single framework. Building upon this insight, we carefully explore the design space of training and sampling, identifying key factors that contribute to both performance and efficiency. Based on the improvements observed during this exploration, we develop our model, referred to as \textbf{eMIGM}. Empirically, eMIGM demonstrates strong performance on ImageNet generation, as measured by Fréchet Inception Distance (FID). In particular, on ImageNet $256\times256$, with similar number of function evaluations (NFEs) and model parameters, eMIGM outperforms the seminal VAR. Moreover, as NFE and model parameters increase, eMIGM achieves performance comparable to the state-of-the-art continuous diffusion model REPA while requiring less than 45\% of the NFE. Additionally, on ImageNet $512\times512$, eMIGM outperforms the strong continuous diffusion model EDM2. Code is available at https://github.com/ML-GSAI/eMIGM.

cs.CV↗
arXiv2026

Failure-Aware Bimanual Teleoperation via Conservative Value Guided Assistance

Teleoperation of high-precision manipulation is con-strained by tight success tolerances and complex contact dy-namics, which make impending failures difficult for human operators to anticipate under partial observability. This paper proposes a value-guided, failure-aware framework for bimanual teleoperation that provides compliant haptic assistance while pre-serving continuous human authority. The framework is trained entirely from heterogeneous offline teleoperation data containing both successful and failed executions. Task feasibility is mod-eled as a conservative success score learned via Conservative Value Learning, yielding a risk-sensitive estimate that remains reliable under distribution shift. During online operation, the learned success score regulates the level of assistance, while a learned actor provides a corrective motion direction. Both are integrated through a joint-space impedance interface on the master side, yielding continuous guidance that steers the operator away from failure-prone actions without overriding intent. Experimental results on contact-rich manipulation tasks demonstrate improved task success rates and reduced operator workload compared to conventional teleoperation and shared-autonomy baselines, indicating that conservative value learning provides an effective mechanism for embedding failure awareness into bilateral teleoperation. Experimental videos are available at https://www.youtube.com/watch?v=XDTsvzEkDRE

cs.RO↗