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Meng Guo

Publications and source records attributed to Meng Guo.

At least 19 recordsLinked to original sources
arXiv2026

BSN-VIII: Detailed Photometric Modeling of Ten W UMa Contact Binaries and a Revised Empirical Period-Mass Relationship

This study continues our ongoing research on contact binary systems by presenting a detailed analysis of 10 targets. Ground-based observations from six different observatories were conducted and used together with TESS data for the analysis process. Photometric data from our observations were reduced with the recently developed AutoWISP pipeline, yielding high-quality light curves with reliable precision for analysis. An investigation of orbital period variations identifies long-term trends in six of the ten analyzed binaries, including three that also display cyclic variations. Four targets show essentially constant orbital periods. The secular trends are attributed to mass transfer. The cyclic modulations in three systems are caused by either magnetic activity cycles or the Light-Travel Time Effect (LTTE) of a third body, while that in the remaining system is solely due to the LTTE. The BSN application was used to model the photometric light curves of the 10 target binaries. Iterative fitting and MCMC refinement provided robust estimates of the system parameters, while starspot modeling was applied for systems showing O'Connell-effect asymmetries. We refine the empirical orbital period-mass relationship for short-period contact binaries by analyzing a homogeneous dataset of systems and deriving an updated primary-mass calibration based on the spectroscopic subset. Using this calibrated relation, the fundamental parameters of the studied systems were subsequently estimated.

astro-ph.SR↗
arXiv2026

Resistance Curvature: Recognition, Polyhedral Structure, and Graph Products

Resistance curvature, introduced by Devriendt and Lambiotte, is a novel discrete curvature notion defined through effective resistance. A graph is called resistance nonnegative if there exists a choice of positive edge weights for which the resistance curvature is nonnegative at every vertex. This property has a notable combinatorial interpretation in terms of random spanning trees: a graph is resistance nonnegative if and only if it admits a distribution on its spanning trees under which every vertex has expected degree at most two. The resistance nonnegativity can also be characterized by the tree double matching polytope. These characterizations reveal strong connections among resistance curvature, effective resistance, spanning tree distributions, matching theory, and polyhedral combinatorics. Based on the sign of the curvature, Devriendt introduced the classes of resistance nonnegative (RN), resistance positive (RP), and strictly resistance nonnegative (SRN) graphs, and posed several questions concerning their recognition, polyhedral structure, and structural properties. In this paper, we first answer Devriendt's question on the computational complexity of recognizing RN, RP, and SRN graphs by proving that all three classes can be recognized in polynomial time. We then address his question concerning the tree double matching polytope $\Theta(G)$. Further, we characterize the vertices of $\Theta(G)$ in terms of full-rank systems of tight constraints. Whenever $\Theta(G)\neq\emptyset$, we also determine the least positive integer $k_G$ such that $k_G\Theta(G)$ is a lattice polytope. Finally, for every finite Cartesian product of paths, we explicitly construct an average point satisfying the condition for resistance nonnegativity, thereby obtaining that such graphs are RN. We further characterize the classes of such Cartesian product graphs that are RP or SRN.

math.CO↗
arXiv2026

FlexWorm: Primitive-augmented Hybrid Contact-motion Planning for Suction-based Multi-segment Deformable Robots

Multi-segment suction-based soft robots are promising for inspection and maintenance in confined or fragile environments, but existing approaches still depend heavily on manually designed gaits and environment-specific motion scripts. This work presents a planning framework for serial multi-segment soft robots with deformable body segments and boundary suction pads. The formulation targets full 3D navigation on complex surfaces and explicitly handles discrete adhesion switching and continuous body deformation under geometric, collision, and quasi-static feasibility constraints, while remaining agnostic to the specific actuation realization used to produce segment deformation. Its core, block-wise IK hybrid search (IKHS), performs best-first search over feasible adhesion transitions while solving inverse kinematics only on induced free blocks. On top of IKHS, primitive-augmented hybrid search (PaHS) uses a learned observation--primitive embedding to retrieve short validated motion segments for fast local proposal, with fallback to standard IKHS branching when retrieval fails. In simulation, the framework consistently outperforms controlled baselines in planning success, transition quality, and efficiency across diverse terrains. PaHS matches IKHS in success rate while substantially reducing planning time. Repeated hardware experiments on a pneumatic multi-segment soft robot further demonstrate executability and online recovery under actuation and adhesion uncertainty.

cs.RO↗
arXiv2026

Homotopy-Aware Corridor Generation without Predefined Reference Paths

Generating safe corridors is essential for collision-free robotic motion planning, yet most existing methods rely on predefined reference paths, which bias corridor geometry and implicitly limit the homotopy classes that can be explored. We propose a reference-path-free corridor generation framework on graphs of convex sets (GCS) that constructs corridors directly as sequences of convex sets, allowing corridor structure to emerge from the free-space representation rather than from a guiding path. To reason about similarity among corridors, we extend visibility-based deformation from paths to convex-set sequences, enabling the fusion of topologically redundant corridors while preserving distinct alternatives. To overcome the limited adaptability of existing GCS methods based on static global decompositions, we further develop an adaptive multi-scale GCS, in which a sampling-based fine-scale graph supports localized updates and a visibility-based coarse-scale graph enables compact global exploration. The two levels maintain topological consistency, allowing incremental updates without full graph reconstruction under environmental uncertainty. Numerical experiments characterize GCS construction, corridor generation, homotopy-aware exploration, and local updates, showing efficient graph construction, stable trajectory-level performance, and shorter-duration homotopy-aware trajectories than existing baselines. Hardware experiments on ground and aerial robots, including deployment with onboard localization, further validate the framework under translated and previously unknown obstacles.

cs.RO↗
arXiv2026

Bolometric correction factor and radiative efficiency for the super-Eddington accretion flow in tidal disruption events

The estimate of the bolometric luminosity and the radiative efficiency are two key aspects for understanding the properties of the accretion flow around a supermassive black hole (BH). In this paper, we focus on the estimate of the bolometric luminosity and the radiative efficiency of the early super-Eddington accretion flow in tidal disruption events (TDEs). Specifically, we first perform radiation hydrodynamic simulations of super-Eddington accretion flow in TDE environment, and then calculate the corresponding emergent spectra with the method of post processing for the simulation data. Based on the emergent spectra, we calculate the isotropic-equivalent X-ray bolometric correction factor $k_\mathrm{bol}$ and the radiative efficiency $\eta$ of the super-Eddington accretion flow. We find that both $k_\mathrm{bol}$ and $\eta$ are BH mass and viewing-angle dependent. $k_\mathrm{bol}$ is in the range of about a few tens to a few thousands, and $\eta$ is in the range of $\sim 10^{-3}-10^{-1}$ for BH mass in the range of $10^{6-7}M_\odot$ and the viewing angle in the range of $0^{\rm o}-90^{\rm o}$. Finally, we apply the derived $k_\mathrm{bol}$ and $\eta$ to some specific TDEs to estimate the accreted mass during an event, which can significantly alleviate the so-called missing energy problem in TDEs.

astro-ph.HE↗
arXiv2026

AMBUSH: Collaborative Capture in Complex Environments with Neural Acceleration

Collaborative capture of dynamic targets is common in nature as an essential strategy for weaker species against the strong. Similar concepts have shown to be useful for numerous robotic applications, such as security and surveillance, search and rescue. However, most existing works focus on analytical and geometric solutions or end-to-end reinforcement learning methods, which are largely constrained to obstacle-free environments or scenarios with sparse, regularly distributed obstacles. This work tackles the problem from a unique perspective: the renowned strategy of``ambush'' alone would suffice for multiple slower pursuers to capture one faster evader with different levels of intelligence efficiently in complex environments. A parameterized strategy of ambush (including discrete and continuous parameters) is designed first, which takes into account the topological properties of the workspace, the truncated line-of-sight visibility, the relative speed ratio and the limited capture range. Then, a Hybrid Monte Carlo Tree Search (H-MCTS) algorithm is proposed to optimize the associated parameters through long-term planning, enabling the identification of highly promising parameters for future capture. Lastly, the neural acceleration is trained offline to learn the ranking of different choices of parameters across various environments, and to directly predict scores, replacing the rollout process in H-MCTS. The neural acceleration is adopted during online H-MCTS to accelerate the planning procedure while guaranteeing the planning quality. Its efficiency and effectiveness are validated in extensive simulations and hardware experiments, against evaders with different capabilities and intelligence levels, including two-times higher velocity and human-controlled behavior.

cs.RO↗
arXiv2026

Domain Knowledge Based Temporal-Spatial Graph Convolution Network for ECG Recognition

In light of strides in Arti cial Intelligence (AI) and its wide spread application, challenges persist in the interpretability of AI models, particularly within specialized domains like healthcare, such as electro cardiograph (ECG) recognition. Rather than relying solely on end-to-end convolutional neural networks, this paper introduces a novel approach using a domain knowledge-based graph convolution network for ECG recognition. Key landmarks points of PRQST, vital to ECG interpreta tion, are incorporated as domain knowledge. The double-stream directed graph is employed to model both intra and inter ECG cycles. Speci cally, spatial directed graphs capture the positional relationships among key points, while temporal directed graphs delineate temporal dependencies between adjacent cycles in extended ECG sequences. Experimental re sults on the First Chinese ECG Intelligent Competition dataset, which speci cally classify ECG into nine categories, prove the e cacy of the proposed model. The overall average F1 score is 88.1%, the average F1 score of rare categories is 76.3%, both outperform the state-of-the-art models. The introduction of domain knowledge did enhance the detec tion performance, especially for rare categories.

cs.LG↗
arXiv2026

A pulsar escaping an ancient open cluster via tidal stripping

Open clusters are the primary birthplaces of stars in the Milky Way disk, yet their neutron star progeny are rarely found within them, presumably due to supernova-induced kicks that eject them at birth. Here we report the arcsec-level localization of the pulsar PSR J1921+3745 to the tidal tail of NGC 6791, one of the oldest and most massive open clusters. Our N-body simulation shows that more than 95% of neutron stars formed in such clusters have been ejected. This pulsar's location in the tidal tail indicates it was retained for billions of years before being stripped by Galactic tides. This long-term retention requires low natal kicks, consistent with formation via electron-capture supernova. Our findings capture a rare snapshot of a neutron star transitioning into the Galactic field, identifying tidal stripping of ancient clusters as a verified source of the Galactic neutron star population.

astro-ph.HE↗
arXiv2026

Action ControlNet: A Lightweight Delay-Aware Adapter for Smooth Asynchronous Control in Vision-Language-Action Models

Vision-language-action (VLA) models have shown strong potential for general-purpose robot manipulation, but their inference latency remains a major obstacle to stable high-frequency control. Asynchronous execution mitigates this bottleneck by overlapping policy inference with action execution, yet the next action chunk is still predicted from stale observations while the robot continues to move. Direct chunk stitching therefore introduces handoff discontinuities, action jitter, and failures in contact-rich manipulation. Existing remedies typically require either full-policy retraining or architecture-specific runtime logic. This work proposes Action ControlNet (ACNet), a lightweight delay-aware adapter that uses the executed motion suffix as a residual condition for a mostly frozen action head. ACNet leaves the pretrained backbone unchanged, introduces few trainable parameters, and remains compatible with generative action heads such as diffusion and flow matching. On Kinetix, Meta-World MT50, and a real-world SO-ARM101 platform, ACNet improves robustness under inference delay and yields smoother asynchronous trajectories than direct chunk stitching, while remaining more lightweight than full delay-conditioned retraining.

cs.RO↗
arXiv2026

Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination

World-Action Models (WAMs) have emerged as a promising paradigm for embodied control by coupling future visual prediction with action generation. However, most existing WAMs rely on photorealistic future prediction, which incurs high inference latency and makes real-time robot deployment difficult. This motivates a more efficient WAM design that preserves the control benefits of future visual prediction while reducing its inference cost. We introduce Efficient-WAM, a World-Action Model that reduces the cost of future imagination while preserving its control benefit. Efficient-WAM improves inference efficiency via a compact video expert transferred from WAN-2.2-5B, token-sparse video latents, and asymmetric video-action denoising that allocates fewer sampling steps to video than to actions. Instead of optimizing the future branch for visual fidelity, Efficient-WAM treats future video prediction as a compact guidance signal for action generation. Comprehensive experiments on RoboTwin 2.0 and real-world manipulation tasks show that Efficient-WAM maintains strong action performance despite visibly coarse future predictions. While maintaining competitive control capabilities, our 1B-parameter model can reduce per-chunk latency to around 100 ms during physical deployment, achieving a 30x speedup over existing WAMs.

cs.RO↗
arXiv2026

HULK: Large-scale Hierarchical Coordination under Continual and Uncertain Temporal Tasks

Multi-agent systems can be extremely efficient when working concurrently and collaboratively, e.g., for delivery, surveillance, search and rescue. Coordination of such teams often involves two aspects: selecting appropriate subteams for different tasks in various areas, and coordinating agents in the subteams to execute the associated subtasks. Existing work often assumes that the tasks are static and known beforehand, where an integer program can be formulated and solved offline. However, in many applications, the team-wise tasks are generated online continually by external requests, and the amount of subtasks within each task is uncertain, e.g., the number of packages to deliver or victims to rescue. The aforementioned offline solution becomes inadequate as it would require constant re-computation for the whole team and global communication to broadcast the results. Thus, this work tackles the large-scale coordination problem under continual and uncertain temporal tasks, specified as temporal logic formulas over collaborative actions. The proposed hierarchical framework, HULK, consists of two interleaved layers: the rolling assignment of currently known tasks to subteams within a certain horizon, and the dynamic coordination within a subteam given the detected subtasks during online execution. Thus, coordination is performed hierarchically at different granularities and triggering conditions, improving computational efficiency and robustness. The method is validated rigorously over large-scale heterogeneous systems under various temporal tasks and environment uncertainties.

cs.RO↗
arXiv2026

Melding LLM and temporal logic for reliable human-swarm collaboration in complex scenarios

Robot swarms promise scalable assistance in complex and hazardous environments. Task planning lies at the core of human-swarm collaboration, translating the operator's intent into coordinated swarm actions and helping determine when validation or intervention is required during execution. In long-horizon missions under dynamic scenarios, however, reliable task planning becomes difficult to maintain: emerging events and changing conditions demand continual adaptation, and sustained operator oversight imposes substantial cognitive burden. Existing LLM-based planning tools can support plan generation, yet they remain susceptible to invalid task orderings and infeasible robot actions, resulting in frequent manual adjustment. Here we introduce a neuro-symbolic framework for long-horizon human-swarm collaboration that tightly melds verifiable task planning with context-grounded LLM reasoning. We formalize mission goals and operational rules as temporal logic formulas and admissible task orderings as task automata. Conditioned on these formal constraints and live perceptual context, LLMs generate executable subtask sequences that satisfy mission rules and remain grounded in the current scene. An uncertainty-aware scheduler then assigns subtasks across the heterogeneous swarm to maximize parallelisms while remaining resilient to disruptions. An event-triggered interaction protocol further limits operator involvement to sparse, high-level confirmation and guidance. Deployment on a heterogeneous robotic fleet yields similar results while remaining robust to hardware-specific actuation and communication uncertainties. Together, these results support a formal and scalable paradigm for reliable and low-overhead human-swarm collaboration in dynamic environments

cs.RO↗
arXiv2026

A Python/CuPy Software Correlator for QUEST: Real-Time Performance and Initial Imaging

We present a Python/CuPy FX software correlator for small radio interferometer arrays and evaluate it on QUEST (Qilu University Explorer Survey Telescope). The system combines multi-threaded data ingest, pinned-memory host-device transfers, GPU-accelerated correlation, Polyphase Filter Bank channelization, MAD-based RFI flagging, and delay/phase calibration in a single workflow aimed at array commissioning. On a single NVIDIA RTX 4090D GPU, the implementation reaches a peak throughput of 1.51 GB/s, which is sufficient for real-time operation in the four-antenna mode tested here. After calibration, the visibility phase across a clean 1.32-1.38 GHz band is flattened to a residual scatter of a few degrees. Using the calibrated visibilities, we form a four-antenna synthesis image of Cassiopeia A; the CLEANed image recovers a compact source at the phase center and reduces image-domain background fluctuations from order 0.1 to a few 0.01 Jy/beam. These results indicate that the software is suitable for small-array commissioning and initial synthesis imaging on QUEST. A GNSS-based beam measurement is included as a supporting commissioning check.

astro-ph.IM↗
arXiv2026

Radiation hydrodynamic simulations for the origin of quasi-periodic oscillations for accretion onto supermassive black holes

Quasi-periodic oscillation (QPO) has been detected in several accreting supermassive black hole (SMBH) systems, including active galactic nuclei (AGNs) and tidal disruption events (TDEs). However, despite that several models have been proposed, the physical origin of QPO is still unclear. In this paper, we performed radiation hydrodynamic simulations of accretion flow by injecting mass at a fixed radius, i.e. 10 Schwarzschild radius with different mass accretion rates, and setting the black hole (BH) mass to $10^7M_{\odot}$. We find that there are QPO signals by analyzing the mass inflow rates as a function of time from the simulations for different radii. The QPO frequencies from our simulations are well consistent with the radial epicyclic frequencies from analytic calculations for radius greater than a critical radius 3.8 Schwarzschild radius. This critical radius corresponds to the maximum epicyclic frequency, i.e. $\nu_{\rm r,max}$, in the radial direction. We proposed that $\nu_{\rm r,max}$ can be a good proxy for the observed QPO $\nu_{\rm QPO}$. Furthermore, assuming that our simulation results can be scaled to different BH masses $M_{\rm BH}$, we find that the theoretical relation of $\nu_{\rm r,max}$ as a function of $M_{\rm BH}$ can well match $\nu_{\rm QPO}$ as a function of $M_{\rm BH}$ for a sample of AGN and TDE. Finally, we discuss the effects of the BH mass, general relativity (GR), and other possible factors including the size of the mass injecting radius, viscosity and magnetic field on the simulation results.

astro-ph.HE↗
arXiv2026

HECTOR: Human-centric Hierarchical Coordination and Supervision of Robotic Fleets under Continual Temporal Tasks

Robotic fleets can be extremely efficient when working concurrently and collaboratively, e.g., for delivery, surveillance, search and rescue. However, it can be demanding or even impractical for an operator to directly control each robot. Thus, autonomy of the fleet and its online interaction with the operator are both essential, particularly in dynamic and partially unknown environments. The operator might need to add new tasks, cancel some tasks, change priorities and modify planning results. How to design the procedure for these interactions and efficient algorithms to fulfill these needs have been mostly neglected in the related literature. Thus, this work proposes a human-centric coordination and supervision scheme (HECTOR) for large-scale robotic fleets under continual and uncertain temporal tasks. It consists of three hierarchical layers: (I) the bidirectional and multimodal protocol of online human-fleet interaction, where the operator interacts with and supervises the whole fleet; (II) the rolling assignment of currently-known tasks to teams within a certain horizon, and (III) the dynamic coordination within a team given the detected subtasks during online execution. The overall mission can be as general as temporal logic formulas over collaborative actions. Such hierarchical structure allows human interaction and supervision at different granularities and triggering conditions, to both improve computational efficiency and reduce human effort. Extensive human-in-the-loop simulations are performed over heterogeneous fleets under various temporal tasks and environmental uncertainties.

cs.RO↗
arXiv2026

UMBRELLA: Uncertainty-aware Multi-robot Reactive Coordination under Dynamic Temporal Logic Tasks

Multi-robot systems can be extremely efficient for accomplishing team-wise tasks by acting concurrently and collaboratively. However, most existing methods either assume static task features or simply replan when environmental changes occur. This paper addresses the challenging problem of coordinating multi-robot systems for collaborative tasks involving dynamic and moving targets. We explicitly model the uncertainty in target motion prediction via Conformal Prediction(CP), while respecting the spatial-temporal constraints specified by Linear Temporal Logic (LTL). The proposed framework (UMBRELLA) combines the Monte Carlo Tree Search (MCTS) over partial plans with uncertainty-aware rollouts, and introduces a CP-based metric to guide and accelerate the search. The objective is to minimize the Conditional Value at Risk (CVaR) of the average makespan. For tasks released online, a receding-horizon planning scheme dynamically adjusts the assignments based on updated task specifications and motion predictions. Spatial and temporal constraints among the tasks are always ensured, and only partial synchronization is required for the collaborative tasks during online execution. Extensive large-scale simulations and hardware experiments demonstrate substantial reductions in both the average makespan and its variance by 23% and 71%, compared with static baselines.

cs.RO↗
arXiv2026

BodyGuards: Escorting by Multiple Robots in Unknown Environment under Limited Communication

Multi-robot systems are increasingly deployed in high-risk missions such as reconnaissance, disaster response, and subterranean operations. Protecting a human operator while navigating unknown and adversarial environments remains a critical challenge, especially when the communication among the operator and robots is restricted. Unlike existing collaborative exploration methods that aim for complete coverage, this work focuses on task-oriented exploration to minimize the navigation time of the operator to reach its goal while ensuring safety under adversarial threats. A novel escorting framework BodyGuards, is proposed to explicitly integrate seamlessly collaborative exploration, inter-robot-operator communication and escorting. The framework consists of three core components: (I) a dynamic movement strategy for the operator that maintains a local map with risk zones for proactive path planning; (II) a dual-mode robotic strategy combining frontier based exploration with optimized return events to balance exploration, threat detection, and intermittent communication; and (III) multi-robot coordination protocols that jointly plan exploration and information sharing for efficient escorting. Extensive human-in-the-loop simulations and hardware experiments demonstrate that the method significantly reduces operator risk and mission time, outperforming baselines in adversarial and constrained environments.

cs.RO↗
arXiv2026

Discovery of a millisecond pulsar with a CO white dwarf companion

We report the discovery and characterization of PSR J1810-0623, a fully recycled millisecond pulsar with a spin period of 4.55 ms, discovered with the Five-hundred-meter Aperture Spherical radio Telescope (FAST) and followed up with FAST and the Green Bank Telescope (GBT). A phase-connected timing solution spanning over 6.5 years reveals a 15.4-day binary orbit with extremely low eccentricity (about 1.5E-5). Assuming a neutron star mass of 1.4 Msun, the inferred companion median mass (about 0.64 Msun) is consistent with a carbon-oxygen white dwarf, indicating an evolutionary origin in an intermediate mass Xray binary. The system's properties closely resemble those of other massive white dwarf binaries thought to form via Case A Roche lobe overflow, suggesting a prolonged accretion phase during which the neutron star was efficiently recycled. Polarimetric analysis of FAST data yields a moderate degree of linear polarization and a rotation measure of 86.6 pm 0.6 rad/m^2, offering constraints on the Galactic magnetic field. The inferred characteristic age (about 32 Gyr) and low surface magnetic field (about 1E8 G) indicate a highly recycled pulsar. Proper-motion measurements imply a modest transverse velocity, consistent with those of recycled millisecond pulsars in the Galactic field. Although the proximity of the globular cluster Pal 7 raises the possibility of a dynamical origin, discrepancies in dispersion measure and proper motion argue against a physical association. PSR J1810-0623 adds to the rare class of long-orbital period MSP-COWD systems and provides a valuable laboratory for studying pulsar recycling, binary evolution, and Galactic structure.

astro-ph.HE↗