arXiv subjects

Liang Wu

Publications and source records attributed to Liang Wu.

At least 55 records · Page 3Linked to original sources

arXiv2026

$π$MPC: A Parallel-in-horizon and Construction-free NMPC Solver

The alternating direction method of multipliers (ADMM) has gained increasing popularity in embedded model predictive control (MPC) due to its code simplicity and pain-free parameter selection. However, existing ADMM solvers either target general quadratic programming (QP) problems or exploit sparse MPC formulations via Riccati recursions, which are inherently sequential and therefore difficult to parallelize for long prediction horizons. This technical note proposes a novel \textit{parallel-in-horizon} and \textit{construction-free} nonlinear MPC algorithm, termed $π$MPC, which combines a new variable-splitting scheme with a velocity-based system representation in the ADMM framework, enabling horizon-wise parallel execution while operating directly on system matrices without explicit MPC-to-QP construction. Numerical experiments and accompanying code are provided to validate the effectiveness of the proposed method.

math.OC↗

arXiv2026

Towards Resilient and Autonomous Networks: A BlueSky Vision on AI-Native 6G

The proliferation of emerging applications, such as autonomous driving and immersive experiences, demands cellular networks that are not only faster, but fundamentally more resilient and autonomous. This paper presents a BlueSky vision on how Artificial Intelligence will be natively integrated into 6G, shifting the paradigm from \underline{Network for AI} to \underline{AI for Network}. We envision that, unlike 5G's reliance on scattered, ad-hoc models each trained for a single task, native AI in the 6G era will be anchored by a foundation model and and orchestrated via collaborative multi-agent systems, framing network management as a unified, multi-modal, multi-task optimization problem. Built on this vision, we outline two transformative directions. The first focuses on developing a 6G foundation model as a unified backbone, with task-specific knowledge distilled into compact models suited for diverse edge deployments. The second advances multi-agent systems designed to autonomously diagnose, maintain, and recover networks with minimal human intervention. These directions chart a roadmap for 6G to evolve into an intelligent, self-sustaining communication infrastructure.

cs.AI↗

arXiv2026

Accelerated and data-efficient flow prediction in stirred tanks via physics-informed learning

The simulation of fluid flows is computationally expensive due to the complexity of its governing partial differential equations. Machine learning models offer a potential surrogate, enabling learning from simulations and significantly faster predictions of flow fields. However, these models require large training datasets, which introduces a trade-off between dataset generation cost and predictive accuracy. In this work, we investigate the relationship between the size of the training-set and accuracy of the prediction when learning steady flow fields in an industrial-scale stirred vessel. A data set of steady flows is generated using Reynolds Averaged Navier Stokes (RANS) simulations in a range of realistic operating conditions, including impeller speeds and liquid heights. We train implicit neural representations of flow fields and compare purely data-driven and constrained variants. Model performance is evaluated using global mean squared error (MSE), qualitative spatial comparisons of predicted and reference flow fields, and tracer transport simulations. We find that the prediction error decreases monotonically with increasing training data, but also that it exhibits clear diminishing returns beyond moderate dataset sizes. Physics-based constraints significantly improve accuracy and reduce variability across training runs in low-data regimes, and they lead to more stable tracer-transport behavior. Furthermore, reasonable interpolation can be achieved over different impeller speeds and liquid heights. However, these benefits come with an increase in the complexity of training, and their relative advantage diminishes as the training set grows.

cs.CE↗

arXiv2026

Safe, or Simply Incapable? Rethinking Safety Evaluation for Phone-Use Agents

When a phone-use agent avoids harm, does that show safety, or simply inability to act? Existing evaluations often cannot tell. A harmful outcome may be avoided because the agent recognized the risk and chose the safe action, or because it failed to understand the screen or execute any relevant action at all. These cases have different causes and call for different fixes, yet current benchmarks often merge them under task success, refusal, or final harmful outcome. We address this problem with PhoneSafety, a benchmark of 700 safety-critical moments drawn from real phone interactions across more than 130 apps. Each instance isolates the next decision at a risky moment and asks a simple question: does the model take the safe action, take the unsafe action, or fail to do anything useful? We evaluate eight representative phone-use agents under this framework. Our results reveal two main patterns. First, stronger general phone-use ability does not reliably imply safer choices at risky moments. Models that perform better on ordinary app tasks are not always the ones that behave more safely when the next action matters. Second, failures to do anything useful behave like a capability signal rather than a safety signal: they are concentrated in more visually and operationally demanding settings and remain stable when the evaluation protocol changes. Across models, failures split into two recurring patterns: unsafe choices in settings where the model can act but chooses wrongly, and inability to act in more visually and operationally demanding screens. Overall, a harmless outcome is not enough to count as evidence of safety. Evaluating phone-use agents requires separating unsafe judgment from inability to act.

cs.CL↗

arXiv2026

Weak localization and universal conductance fluctuations in large area twisted bilayer graphene

We study diffusive magnetotransport in highly p-doped large area twisted bilayer graphene in 1°, 7°, 9° and 20° samples. We report weak localization in twisted bilayer graphene for the first time. All samples exhibit weak localization, from which we extract the phase coherence length and intervalley scattering lengths, and from that determine that dephasing is caused by electron-electron scattering and intervalley scattering is caused by point defects. We observe signatures of universal conductance fluctuations in the 9° sample, which has high mobility and is near the van Hove singularity. Further improvements in sample quality and applications to large area moire materials will open new avenues to observe quantum interference effects.

cond-mat.mes-hall↗

arXiv2026

Towards Real-World Document Parsing via Realistic Scene Synthesis and Document-Aware Training

Document parsing has recently advanced with multimodal large language models (MLLMs) that directly map document images to structured outputs. Traditional cascaded pipelines depend on precise layout analysis and often fail under casually captured or non-standard conditions. Although end-to-end approaches mitigate this dependency, they still exhibit repetitive, hallucinated, and structurally inconsistent predictions - primarily due to the scarcity of large-scale, high-quality full-page (document-level) end-to-end parsing data and the lack of structure-aware training strategies. To address these challenges, we propose a data-training co-design framework for robust end-to-end document parsing. A Realistic Scene Synthesis strategy constructs large-scale, structurally diverse full-page end-to-end supervision by composing layout templates with rich document elements, while a Document-Aware Training Recipe introduces progressive learning and structure-token optimization to enhance structural fidelity and decoding stability. We further build Wild-OmniDocBench, a benchmark derived from real-world captured documents for robustness evaluation. Integrated into a 1B-parameter MLLM, our method achieves superior accuracy and robustness across both scanned/digital and real-world captured scenarios. All models, data synthesis pipelines, and benchmarks will be publicly released to advance future research in document understanding.

cs.CV↗

arXiv2026

Channel Knowledge Map-Enabled NLoS ISAC Localization

Accurate localization in non-line-of-sight (NLoS) environments remains challenging even with both angle-of-arrival (AoA) and time-of-arrival (ToA) measurements. In complex urban scenarios, the absence of line-of-sight (LoS) paths and the lack of environment prior knowledge make geometric based localization methods inapplicable, while prior-based approach such as fingerprinting is sensitive to environmental perturbations. This paper proposes a novel environment-aware localization framework enabled by the emerging concept called channel knowledge map (CKM). In the offline stage, AoA-ToA path signatures are learned by the CKM, with each path mapped to one candidate scatterer, thereby forming geometric priors within the environment. In the online stage, observed paths are matched to the CKM to extract high-confidence scatterers. Nonlinear least squares (NLS) method is then applied to jointly estimate the user and dominant scatterer locations. Even with imperfect CSI matching, geometric feasibility consistent with CKM scatterer priors provides corrective information and suppresses ambiguity. Simulations demonstrate that the proposed scheme outperforms fingerprinting and offers a robust and scalable solution to address the challenging NLoS localization for integrated sensing and communication (ISAC) systems.

eess.SP↗

arXiv2026

Fixed-time-stable ODE Representation of Lasso

Lasso problems arise in many areas, including signal processing, machine learning, and control, and are closely connected to sparse coding mechanisms observed in neuroscience. A continuous-time ordinary differential equation (ODE) representation of the Lasso problem not only enables its solution on analog computers but also provides a framework for interpreting neurophysiological phenomena. This article proposes a fixed-time-stable ODE representation of the Lasso problem by first transforming it into a smooth nonnegative quadratic program (QP) and then designing a projection-free Newton-based ODE representation of the Lasso problem by first transforming it into a smooth nonnegative quadratic program (QP) and then designing a projection-free Newton-based fixed-time-stable ODE system for solving the corresponding Karush-Kuhn-Tucker (KKT) conditions. Moreover, the settling time of the ODE is independent of the problem data and can be arbitrarily prescribed. Numerical experiments verify that the trajectory reaches the optimal solution within the prescribed time.

math.OC↗

arXiv2026

Polynomial Parametric Koopman Operators for Stochastic MPC

This paper develops a parametric Koopman operator framework for Stochastic Model Predictive Control (SMPC), where the Koopman operator is parametrized by Polynomial Chaos Expansions (PCEs). The model is learned from data using the Extended Dynamic Mode Decomposition -- Dictionary Learning (EDMD-DL) method, which preserves the convex least-squares structure for the PCE coefficients of the EDMD matrix. Unlike conventional stochastic Galerkin projection approaches, we derive a condensed deterministic reformulation of the SMPC problem whose dimension scales only with the control horizon and input dimension, and is independent of both the lifted state dimension and the number of retained PCE terms. Our framework, therefore, enables efficient nonlinear SMPC problems with expectation and second-order moment constraints with standard convex optimization solvers. Numerical examples demonstrate the efficacy of our framework for uncertainty-aware SMPC of nonlinear systems.

eess.SY↗

arXiv2026

Tracing Star Formation in Quasar Hosts via [O II] $λ$3727: A Kinematically Consistent Approach

Measuring star formation in quasar host galaxies is crucial for understanding the coevolution of supermassive black holes (SMBHs) and galaxies, yet remains observationally challenging due to severe contamination from active galactic nucleus (AGN) emission. In this work, we present a new method to robustly isolate the AGN contribution to the [O II] $λ$3727 emission line in quasars, based on a kinematically consistent decomposition of [O II] and the high-ionization [Ne V] $λ$3426 line. We find that the [O II] emission in quasars is primarily dominated by star formation, with only a weak AGN contribution, and thus can be reliably used as a tracer of star formation in quasar hosts. Applying this technique to a large sample of Sloan Digital Sky Survey quasars, we derive mean SFRs as a function of bolometric luminosity. We find a tight correlation between mean SFR and luminosity. Further analysis, assuming a constant dust extinction correction to [O II] emission, shows that luminosity is the primary parameter most strongly associated with star formation, rather than SMBH mass or Eddington ratio. This supports the scheme in which star formation and black hole accretion are closely linked through their common dependence on the cold gas supply.

astro-ph.GA↗

arXiv2026

Intrinsic Even-Odd Thickness-Driven Anomalous Hall in Epitaxial MnBi2Te4 Thin Films

We demonstrate precise control of magnetism in MnBi2Te4 thin films through careful synthesis by molecular beam epitaxy, achieving minimal defects and accurate layer thickness control. By optimizing Mn-Bi-Te ratios and growth temperatures, we minimize detrimental self-doping effects and accurately target integer-layer films. X-ray diffraction and reflectivity provide quantitative measures of film quality and thickness. When these macroscale probes of structure and thickness are integrated with magnetotransport measurements, a striking even-odd layer dependence of the anomalous Hall effect is revealed. Odd-layer films exhibit a large hysteresis up to the Néel temperature (~25K), consistent with non-compensated antiferromagnetism, while even-layer films show minimal response, as expected for an antiferromagnet. The sign of the anomalous Hall effect exhibits a sign reversal for intrinsic magnetism versus magnetism associated with defects. This work identifies critical factors for inducing pure, non-compensated ferromagnetism and reveals the character of the intrinsic anomalous Hall effect in MnBi2Te4, which together is a step towards realizing the zero-field quantum anomalous Hall effect in topological materials.

cond-mat.mtrl-sci↗

arXiv2026

Koopman-BoxQP: Solving Large-Scale NMPC at kHz Rates

Solving large-scale nonlinear model predictive control (NMPC) problems at kilohertz (kHz) rates on standard processors remains a formidable challenge. This paper proposes a Koopman-BoxQP framework that i) learns a linear Koopman high-dimensional model, ii) eliminates the high-dimensional observables to construct a multi-step prediction model of the states and control inputs, iii) penalizes the multi-step prediction model into the objective, which results in a structured box-constrained quadratic program (BoxQP) whose decision variables include both the system states and control inputs, iv) develops a structure-exploited and warm-starting-supported variant of the feasible Mehrotra's interior-point algorithm for BoxQP. Numerical results demonstrate that Koopman-BoxQP can solve a large-scale NMPC problem with $1040$ variables and $2080$ inequalities at a kHz rate.

eess.SY↗

arXiv2026

Observation of Room-temperature Charge Density Wave Correlations via Coherent Phonon Spectroscopy in Sn-doped Kagome Superconductor CsV$_3$Sb$_5$

In this work, we perform ultrafast time-resolved reflectivity measurements to track the evolution of charge density wave (CDW) correlations in Sn-doped Kagome superconductor CsV$_3$Sb$_{5-x}$Sn$_x$. By extracting the coherent phonon spectrum, we evidence robust signatures of CDW correlations at temperature and doping ranges far beyond the phase boundary of long-range CDW order. Remarkably, we unveil short-range CDW correlations survive up to room temperature in $x = 0.32$ Sn-doped CsV$_3$Sb$_5$, supported by synchrotron X-ray diffraction measurements. We point out the introduction of quenched disorder by Sn doping can pin the CDW and form static short-range CDW, which can explain the observed persistent CDW signatures. Our results thus corroborate the ubiquity and robustness of CDW correlations in Sn-doped CsV$_3$Sb$_5$ and provide new insights on the role of disorders on the CDW correlations in AV$_3$Sb$_5$ family.

cond-mat.str-el↗

arXiv2026

Revealing Rotational Symmetry Breaking Charge-density Wave Order in Kagome Superconductor (Rb, K)V$_3$Sb$_5$ by Ultrafast Pump-probe Experiments

The recently discovered Kagome superconductor AV$_3$Sb$_5$ (where A refers to K, Rb, Cs) has stimulated widespread research interest due to its interplay of non-trivial topology and unconventional correlated physics including charge-density waves (CDW) and superconductivity. The essential prerequisite to understanding the microscopic mechanisms of this complex electronic landscape is to unveil the configuration and symmetry of the charge-density wave order. As to now, little consensus has been made on what symmetry is broken. Herein, we clarify the microscopic structure and symmetry breaking of the CDW phase in RbV$_3$Sb$_5$ and KV$_3$Sb$_5$ by ultrafast time-resolved reflectivity. Our approach is based on extracting coherent phonon spectra induced by three-dimensional CDW and comparing them to calculated phonon frequencies via density-functional theory. The combination of these experimental results and calculations provides compelling evidence that the CDW structure of both compounds prevailing up to T$_{\text{CDW}}$ is the 2 $\times$ 2 $\times$ 2 staggered inverse Star-of-David pattern with interlayer $π$ phase shift, in which the six-fold rotational symmetry is broken. These observations thus corroborate six-fold rotational symmetry breaking throughout the CDW phase of RbV$_3$Sb$_5$ and KV$_3$Sb$_5$.

cond-mat.str-el↗

arXiv2026

Time-Certified and Efficient NMPC via Koopman Operator

Certifying and accelerating execution times of nonlinear model predictive control (NMPC) implementations are two core requirements. Execution-time certificate guarantees that the NMPC controller returns a solution before the next sampling time, and achieving faster worst-case and average execution times further enables its use in a wider set of applications. However, NMPC produces a nonlinear program (NLP) for which it is challenging to derive its execution time certificates. Our previous works, \citep{wu2025direct,wu2025time} provide data-independent execution time certificates (certified number of iterations) for box-constrained quadratic programs (BoxQP). To apply the time-certified BoxQP algorithm \citep{wu2025time} for state-input constrained NMPC, this paper i) learns a linear model via Koopman operator; ii) proposes a dynamic-relaxation construction approach yields a structured BoxQP rather than a general QP; iii) exploits the structure of BoxQP, where the dimension of the linear system solved in each iteration is reduced from $5N(n_u+n_x)$ to $Nn_u$ (where $n_u, n_x, N$ denote the number of inputs, states, and length of prediction horizon), yielding substantial speedups (when $n_x \gg n_u$, as in PDE control).

eess.SY↗

arXiv2026

Efficient and Robust p-type Transistor based on Ultra-wide-bandgap Semiconductor

The p-type transistor is an indispensable component of semiconductor technology, enabling complementary operation with n-channel transistors for computation, storage, and communication. Achieving both high robustness and high efficiency is highly desirable but challenging for p-type transistors due to limited semiconductors with reliable hole transport and their high activation energies. Here, we achieved a robust yet efficient p-type transistor by heterogeneously integrating an ultra-wide-bandgap semiconductor and a high-k dielectric layer through van der Waals integration. The p-type transistor employs a two-dimensional hole channel on hydrogenated diamond (bandgap 5.6 eV) combined with a high-k (30.5) SrTiO3 perovskite membrane. At room temperature, the transistor exhibits stable operation with a high on-current (~200 mA/mm), low subthreshold swing (70 mV/dec), high hole mobility (566 cm^2/Vs to 572 cm^2/Vs) and high on-off ratio (~10^9). Furthermore, tuning annealing temperature allows operation in either enhancement or depletion mode. The robust p-type transistor with high efficiency holds great potential for future power electronics, UV optoelectronics, and harsh-environment electronic applications.

cond-mat.mtrl-sci↗

arXiv2026

A Generalist Foundation Model for Total-body PET/CT Enables Diagnostic Reporting and System-wide Metabolic Profiling

Total-body PET/CT enables system-wide molecular imaging, but heterogeneous anatomical and metabolic signals, approximately 2 m axial coverage, and structured radiology semantics challenge existing medical AI models that assume single-modality inputs, localized fields of view, and coarse image-text alignment. We introduce SDF-HOLO (Systemic Dual-stream Fusion Holo Model), a multimodal foundation model for holistic total-body PET/CT, pre-trained on more than 10,000 patients. SDF-HOLO decouples CT and PET representation learning with dual-stream encoders and couples them through a cross-modal interaction module, allowing anatomical context to refine PET aggregation while metabolic saliency guides subtle morphological reasoning. To model long-range dependencies across the body, hierarchical context modeling combines efficient local windows with global attention. To bridge voxels and clinical language, we use anatomical segmentation masks as explicit semantic anchors and perform voxel-mask-text alignment during pre-training. Across tumor segmentation, low-dose lesion detection, and multilingual diagnostic report generation, SDF-HOLO outperforms strong task-specific and clinical-reference baselines while reducing localization errors and hallucinated findings. Beyond focal interpretation, the model enables system-wide metabolic profiling and reveals tumor-associated fingerprints of inter-organ metabolic network interactions, providing a scalable computational foundation for total-body PET/CT diagnostics and system-level precision oncology.

cs.CV↗

arXiv2026

Least-Squares Multi-Step Koopman Operator Learning for Model Predictive Control

MPC is widely used in real-time applications, but practical implementations are typically restricted to convex QP formulations to ensure fast and certified execution. Koopman-based MPC enables QP-based control of nonlinear systems by lifting the dynamics to a higher-dimensional linear representation. However, existing approaches rely on single-step EDMD. Consequently, prediction errors may accumulate over long horizons when the EDMD operator is applied recursively. Moreover, the multi-step prediction loss is nonconvex with respect to the single-step EDMD operator, making long-horizon model identification particularly challenging. This paper proposes a multi-step EDMD framework that directly learns the condensed multi-step state-control mapping required for Koopman-MPC, thereby bypassing explicit identification of the lifted system matrices and subsequent model condensation. The resulting identification problem admits a convex least-squares formulation. We further show that the problem decomposes across prediction horizons and state coordinates, enabling parallel computation and row-wise $\ell_1$-regularization for automatic dictionary pruning. A non-asymptotic finite-sample analysis demonstrates that, unlike one-step EDMD, the proposed method avoids error compounding and yields error bounds that depend only on the target multi-step mapping. Numerical examples validate improved long-horizon prediction accuracy and closed-loop performance.

eess.SY↗