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Long Wang

Publications and source records attributed to Long Wang.

At least 55 records · Page 3Linked to original sources
arXiv2026

Integrated Shape-Force Estimation for Continuum Robots: A Virtual-Work and Polynomial-Curvature Framework

Cable-driven continuum robots (CDCRs) are widely used in surgical and inspection tasks that require dexterous manipulation in confined spaces. Existing model-based estimation methods either assume constant curvature or rely on geometry-space interpolants, both of which struggle with accuracy under large deformations and sparse sensing. This letter introduces an integrated shape-force estimation framework that combines cable-tension measurements with tip-pose data to reconstruct backbone shape and estimate external tip force simultaneously. The framework employs polynomial curvature kinematics (PCK) and a virtual-work-based static formulation expressed directly in curvature space, where polynomial modal coefficients serve as generalized coordinates. The proposed method is validated through Cosserat-rod-based simulations and hardware experiments on a torque-cell-enabled CDCR prototype. Results show that the second-order PCK model achieves superior shape and force accuracy, combining a lightweight shape optimization with a closed-form, iteration-free force estimation, offering a compact and robust alternative to prior constant-curvature and geometry-space approaches.

cs.RO↗
arXiv2026

PiLoT: Neural Pixel-to-3D Registration for UAV-based Ego and Target Geo-localization

We present PiLoT, a unified framework that tackles UAV-based ego and target geo-localization. Conventional approaches rely on decoupled pipelines that fuse GNSS and Visual-Inertial Odometry (VIO) for ego-pose estimation, and active sensors like laser rangefinders for target localization. However, these methods are susceptible to failure in GNSS-denied environments and incur substantial hardware costs and complexity. PiLoT breaks this paradigm by directly registering live video stream against a geo-referenced 3D map. To achieve robust, accurate, and real-time performance, we introduce three key contributions: 1) a Dual-Thread Engine that decouples map rendering from core localization thread, ensuring both low latency while maintaining drift-free accuracy; 2) a large-scale synthetic dataset with precise geometric annotations (camera pose, depth maps). This dataset enables the training of a lightweight network that generalizes in a zero-shot manner from simulation to real data; and 3) a Joint Neural-Guided Stochastic-Gradient Optimizer (JNGO) that achieves robust convergence even under aggressive motion. Evaluations on a comprehensive set of public and newly collected benchmarks show that PiLoT outperforms state-of-the-art methods while running over 25 FPS on NVIDIA Jetson Orin platform. Our code and dataset is available at: https://github.com/Choyaa/PiLoT.

cs.CV↗
arXiv2026

Node bipartition for rigidity and localization of networks with heterogeneous sensing

Graph rigidity theory is an important tool for examining the solvability of sensor network localization (SNL) problems, and ensuring global convergence of localization algorithms. Along this direction, diverse measurements such as signed angle (SA) and ratio of distance (RoD) have been considered. However, little is known about how the bipartition of nodes based on perceptual abilities affects the rigidity property of the network. In this paper, we study the rigidity and localization of networks with heterogeneous nodes, namely, two types of sensors measuring SA and RoD, respectively. Interestingly, the rigidity property is shown to be strongly dependent on the bipartition of nodes, and exhibits a duality. Moreover, an SA-RoD constrained network can be uniquely determined up to uniform rotations, translations, and scalings (global SA-RoD rigidity) even if it is neither SA rigid nor RoD rigid. A scalable approach to construction of globally SA-RoD rigid frameworks is proposed. Localizability analysis and localization algorithm synthesis are both conducted based on weaker network topology conditions, compared with SA- or RoD-based SNL approaches. Numerical simulations are worked out to validate the theoretical results.

math.OC↗
arXiv2026

Mock Observations of Multiple Stellar Populations in Tidal Streams of Palomar 5 for the Chinese Space Station Survey Telescope

Observations show that multiple stellar populations (MPs) are ubiquitous in globular clusters. The Hubble Space Telescope (HST) has been a pivotal tool for previous photometric studies of MPs. The Chinese Space Station Survey Telescope (CSST) is a two-meter telescope scheduled for launch. One of its imaging instruments, the Survey Camera (SC), combines ultraviolet sensitivity comparable to that of HST with a significantly larger field of view, making it well-suited for conducting large-scale photometric surveys of MPs within extensive stellar stream structures. In this work, we perform mock observations of the stellar stream Palomar 5 to assess the feasibility of detecting MPs with the CSST/SC. The results indicate that the CSST/SC cannot resolve MPs in stellar streams at distances comparable to Palomar 5 ($\gtrsim 20$ kpc) with one or ten 150 s exposures. This fundamental limitation arises from the absence of the precise proper motions required to disentangle stream members. We estimate that successful resolution would require the target stream to be $\lesssim$ 8 kpc under a 150 s exposure. Furthermore, using theoretical color-magnitude diagrams, we find that the CSST/SC $g$-band provides an optimal balance between contamination rate and completeness rate for member identification in the cluster's core. However, this approach fails in the stream due to severe field star contamination. Therefore, future CSST observations of Palomar 5 and its tidal tails will employ multiple epochs across several bands to obtain the deep photometry and proper motion data for a definitive MP analysis.

astro-ph.IM↗
arXiv2026

Binary disruption during the early phase of open clusters

The binary fraction in young open clusters exceeds that of field stars, making the study of binary dynamical evolution in clusters essential for understanding the origins and evolution of field binaries. Using N-body simulations based on Gaia DR3 open cluster observations and assuming a 100\% primordial binary fraction, we investigated the early evolution of binary survival fractions in open clusters. We find that binary disruption has two stages, an initial rapid decline followed by a slower decrease, well described by two piecewise linear functions. The early disruption rate, $k_1$, follows a power-law relation with the cluster's initial density ($ ρ_\mathrm{0} $), with an index of approximately 0.56, driven by the disruption of wide binaries via close encounters. The transition time between the two phases, $t_\mathrm{b}$, also exhibits a power-law dependence on $ρ_\mathrm{0}$ with an index of about -0.46. The disruption rate also depends on binary parameters: high-$q$ and wide binaries are disrupted faster, while the dependence on eccentricity $e$ is less clear, likely due to its strong evolution. We developed and publicly released a Python tool to predict binary survival fraction evolution based on $ρ_0$, $P$ and $q$. Additionally, we also investigate how open cluster binaries contribute to the field population, and find that the escaped stars have a systematically lower binary fraction, likely due to mass segregation. Both populations show similar distributions of $ P $ and $e$, but lower-$q$ systems preferentially remain bound within clusters, the origin of which remains uncertain.

astro-ph.GA↗
arXiv2026

Spatial Property of Multiple Metallic Populations in the Tidal Stream of ω Centauri

ω Centauri, the remnant nucleus of an accreted dwarf galaxy, is a unique laboratory for studying complex stellar populations. The recently discovered Fimbulthul stream provides a fossil record of its ongoing tidal dissolution. In this work, we investigate the spatial distributions of metal-rich and metal-poor populations within ω Centauri and its stream to constrain the cluster's formation history. Using synthetic photometry from Gaia DR3 XP spectra, we classify stars via a Support Vector Classifier (SVC). The spatial distributions are then compared to a scaling N-body simulation performed with the PeTar code. Our analysis reveals no significant radial gradient in population ratios within the cluster, though the metal-rich stars may be slightly more extended. The population ratio in the tidal stream is consistent with that of the present-day cluster, albeit with large uncertainties. Our simulation indicates that any initial radial gradient must have been shallow, with a maximum fraction difference less than 0.15. Both observational and dynamical results suggest that the metal-rich population is not formed centrally concentrated. By combining our results and existing literature, we propose a new formation scenario for ω Centauri.

astro-ph.GA↗
arXiv2026

The primordial nature of the C-19 stellar stream

Stellar streams, remnants of compact star systems stretched out by the tidal forces of the Milky Way, offer a unique way to study stellar populations that formed billions of years ago. A particularly unique stream is C-19, the most metal-poor stellar stream known at less than a thousandth of the Sun's metallicity. The nature of C-19 is not yet clear, with properties that resemble both star clusters and ultra faint dwarf galaxies, yet in either case its extremely low metallicity indicates very early star formation, <1 Gyr after the Big Bang. Here, we present the first detailed study on the nature of C-19 based on the chemical abundances of 14 member stars from high-resolution spectroscopy. These reveal that C-19 formed stars in an early, rapid, and prolific star formation event, with mild inhomogeneous mixing of elements produced in massive stars. There is otherwise no evidence for subsequent star formation, multiple stellar populations, nor chemical evolution. Although C-19 is currently disrupted in the Milky Way halo, it offers a rare and complementary window into the details of star formation and chemical evolution in the early universe, ideal for comparisons with current studies of primordial star formation in the high-redshift universe.

astro-ph.GA↗
arXiv2026

Unseen-Codebases-Domain Data Synthesis and Training Based on Code Graphs

In the context of newly release software frameworks, large language models (LLMs) often exhibit poor performance and a high rate of hallucination, as they are not exposed to such environments during training. Although inference-time augmentation techniques such as retrieval-augmented generation (RAG) can partially mitigate hallucinations, knowledge injection through prompting alone is insufficient to enable models to fully understand the intrinsic relationships among different components of a codebase, or to reason about the correct compositions and apply. Although explicit knowledge injection can be achieved through post-training, compared with public code domains, unseen codebases typically provide only source code and lack large volumes of high-quality, usage-oriented code that can be directly leveraged as training data. Consequently, existing data synthesis approaches are insufficient to adequately capture unseen codebases usage scenarios when restricted to source code alone. To address these challenges, we propose UCD-Training, a two-stage training framework for reasoning-aware data synthesis grounded in a code graph constructed from unseen codebases. UCD-Training first parses the source code to build a code graph, then conducts dependency-preserving continued pretraining (CPT) using file-level dependency data, followed by graph-grounded supervised fine-tuning (SFT) on three types of synthesized data augmented with explicit reasoning traces: (1) single-hop relation reasoning data, (2) compositional API reasoning data, and (3) codebase utilization data. We further introduce a new benchmark, UnseenCodeBench, for code generation on unseen codebases and conduct comprehensive experiments across multiple codebases.

cs.SE↗
arXiv2026

The effective cone conjecture for Calabi--Yau pairs

We formulate an effective cone conjecture for klt Calabi--Yau pairs $(X,Δ)$, pertaining to the structure of the cone of effective divisors $\mathrm{Eff}(X)$ modulo the action of the subgroup of pseudo-automorphisms $\mathrm{PsAut}(X,Δ)$. Assuming the existence of good minimal models in dimension $\dim(X)$, known to hold in dimension up to $3$, we prove that the effective cone conjecture for $(X,Δ)$ is equivalent to the Kawamata--Morrison--Totaro movable cone conjecture for $(X,Δ)$, among other statements. As an application, we show that the movable cone conjecture unconditionally holds for the smooth Calabi--Yau threefolds introduced by Schoen and studied by Namikawa, Grassi and Morrison. We also show that for such a Calabi--Yau threefold $X$, all of its minimal models, apart from $X$ itself, have rational polyhedral nef cones.

math.AG↗
arXiv2026

Kinematic Evidence for Open Cluster Origins of Galactic Binary Neutron Stars

We investigate the potential birthplace of Galactic binary neutron star (BNS) systems through a kinematic analysis. Using high-precision astrometry from Gaia DR3, updated pulsar distances, and Monte Carlo sampling of astrometric errors, we integrate the past trajectories of 11 Galactic BNSs and 167 globular clusters plus 2967 open clusters, to search for past encounters. Our results suggest that BNS origin in globular clusters is unlikely, with low encounter probabilities (e.g., $\lesssim 0.5\%$ for NGC 5139) and requiring excessive ejection velocities. Conversely, our analysis indicates that open clusters are a non-negligible formation channel. Specifically, the double pulsar J0737$-$3039 shows a $13.9\%$ ($5.4\%$) probability of originating from the young cluster OC 0450 (Theia 58). Based on encounter proximity and time, we argue that Theia 58 is its more plausible birthplace. Our work provides kinematic evidence consistent with an open-cluster origin for a subset of field BNSs.

astro-ph.HE↗
arXiv2026

Thompson Sampling-Based Learning and Control for Unknown Dynamic Systems

Thompson sampling (TS) is a Bayesian randomized exploration strategy that samples options (e.g., system parameters or control laws) from the current posterior and then applies the selected option that is optimal for a task, thereby balancing exploration and exploitation; this makes TS effective for active learning-based controller design. However, TS relies on finite parametric representations, which limits its applicability to more general spaces, which are more commonly encountered in control system design. To address this issue, this work proposes a parameterization method for control law learning using reproducing kernel Hilbert spaces and designs a data-driven active learning control approach. Specifically, the proposed method treats the control law as an element in a function space, allowing the design of control laws without imposing restrictions on the system structure or the form of the controller. A TS framework is proposed in this work to reduce control costs through online exploration and exploitation, and the convergence guarantees are further provided for the learning process. Theoretical analysis shows that the proposed method learns the relationship between control laws and closed-loop performance metrics at an exponential rate, and the upper bound of control regret is also derived. Furthermore, the closed-loop stability of the proposed learning framework is analyzed. Numerical experiments on controlling unknown nonlinear systems validate the effectiveness of the proposed method.

cs.LG↗
arXiv2026

The effects of star-gas interactions on binary evolution in open clusters

Star-gas interactions can provide gravitational feedback that influences the dynamical evolution of stellar clusters, through processes such as dynamical friction (DF) and its non-dissipative counterpart, negative dynamical friction (NDF). Using the \texttt{PeTar} code, we perform direct $N$-body simulations of an open cluster initially containing $10^4$ stars, evolving within a gaseous medium spanning a range of ambient densities. Our results demonstrate that NDF associated with stellar outflows interacting with the surrounding gas can enhance the rate of cluster expansion, preferentially transporting stars toward the cluster outskirts. This behavior is accompanied by a more rapid decline in the number of binaries composed of a neutron star and a main-sequence star. A statistical analysis of binary orbital parameters further indicates that, compared to DF-dominated evolution, NDF tends to retain systems with larger semi-major axes and lower eccentricities. Outflow-ambient gas interactions can modify the dynamical processing of binaries in star clusters, leading to changes in the survival fraction and composition of the remaining binary population.

astro-ph.GA↗
arXiv2026

Reflections over the Sea: Reconfigurable Intelligent Surface for Maritime Self-Powered Communications

Maritime communication is becoming a vital component of 6G networks, driven by the rapid expansion of the maritime economy. However, existing technologies face critical challenges in signal coverage, availability, and robustness, especially under harsh sea conditions. This paper proposes a novel framework for the maritime Internet-of-Things (IoT) communications that leverages the reconfigurable intelligent surface (RIS) mounted on offshore infrastructures, such as wind turbines, to enhance coverage and reliability. To capture dynamic maritime environment, a near-ocean-surface channel model is developed considering the impact of sea waves. In addition, a wave energy harvesting (EH) system is designed to self-power IoT sensors for data acquisition, processing, and transmission. To support real-time adaptation, channel state information is continuously measured to optimize RIS reflection parameters and maximize multi-user communication rates. Simulation results show that the proposed system significantly improves IoT communication performance by over 20%, under harsh sea conditions.

cs.IT↗
arXiv2026

Collapse versus Disruption: The Fate of Compact Stellar Systems in Ultralight Dark Matter Halos

Interference of the ultralight dark matter (ULDM) field generates time-varying gravitational potential fluctuations, which stochastically heat stellar systems embedded in ULDM halos. Small-sized stellar systems are therefore often used to set stringent constraints on ULDM. However, the evolution of systems with sizes well below the ULDM de Broglie wavelength remains poorly explored. Using numerical simulations, we show that the evolution of compact stellar systems in ULDM halos is governed by the interplay between internal stellar relaxation and ULDM-induced heating. We find the following main results. First, in sufficiently compact systems, relaxation-driven core collapse dominates, allowing the system to remain bound and dense, while ULDM-induced stripping of outer stars further accelerates the collapse. Second, in more extended systems, ULDM heating dominates and ultimately disrupts the system. Near the disruption threshold, we identify systems resembling ultra-faint dwarfs like Segue 1. Third, we further introduce a dimensionless parameter to quantify the relative importance of heating and relaxation and finally lead to an evolutionary phase diagram. Our results reveal the rich and nontrivial dynamics of compact stellar systems in ULDM halos, indicating that precise system modeling is essential for robust ULDM constraints.

astro-ph.CO↗
arXiv2026

A free-fall-based switching criterion for P^3 T N-body methods in collisional stellar systems

The P$^3$T scheme is a hybrid method for simulating gravitational $N$-body systems. It combines a fast particle-tree (PT) algorithm for long-range forces with a high-accuracy particle-particle (PP, direct $N$-body) solver for short-range interactions. Preserving both PT efficiency and PP accuracy requires a robust PT-PP switching criterion. We introduce a simple free-fall-based switching criterion for general stellar systems, alongside the commonly used velocity-dispersion-based ($σ$-based) criterion. Using the \textsc{petar} code with the P$^3$T scheme and slow-down algorithmic regularization for binaries and higher-order multiples, we perform extensive simulations of star clusters to evaluate how each criterion affects energy conservation and binary evolution. For systems in virial equilibrium, we find that the free-fall-based criterion is generally more accurate for low-$σ$ or loose clusters containing binaries, whereas the $σ$-based criterion is better suited for high-$σ$ systems. Under subvirial or fractal initial conditions, both criteria struggle to maintain high energy conservation; however, the free-fall-based criterion improves as the tree timestep is reduced, whereas the $σ$-based degrades due to its low-accuracy treatment of two-body encounters.

astro-ph.IM↗
arXiv2025

Is Image-based Object Pose Estimation Ready to Support Grasping?

We present a framework for evaluating 6-DoF instance-level object pose estimators, focusing on those that require a single RGB (not RGB-D) image as input. Besides gaining intuition about how accurate these estimators are, we are interested in the degree to which they can serve as the sole perception mechanism for robotic grasping. To assess this, we perform grasping trials in a physics-based simulator, using image-based pose estimates to guide a parallel gripper and an underactuated robotic hand in picking up 3D models of objects. Our experiments on a subset of the BOP (Benchmark for 6D Object Pose Estimation) dataset compare five open-source object pose estimators and provide insights that were missing from the literature.

cs.RO↗
arXiv2025

Aggregative games with bilevel structures: Distributed algorithms and convergence analysis

In this paper, the problem of distributively seeking the equilibria of aggregative games with bilevel structures is studied. Different from the traditional aggregative games, here the aggregation is determined by the minimizer of a virtual leader's objective function in the inner level, which depends on the actions of the players in the outer level. Moreover, the global objective function of the virtual leader is formed by the sum of some local functions with two arguments, each of which is strongly convex with respect to the second argument. When making decisions, each player in the outer level only has access to a local part of the virtual leader's objective function. To handle this problem, first, we propose a second order gradient-based distributed algorithm, where the Hessian matrices associated with the objective functions of the leader are involved. By the algorithm, players update their actions while cooperatively minimizing the objective function of the virtual leader to estimate the aggregation by communicating with their neighbors via a connected graph. Under mild assumptions on the graph and cost functions, we prove that the actions of players asymptotically converge to the Nash equilibrium point. Then, for the case where the Hessian matrices associated with the objective functions of the virtual leader are not available, we propose a first order gradient-based distributed algorithm, where a distributed two-point estimate strategy is developed to estimate the gradients of players' cost functions in the outer level. Under the same conditions, we prove that the convergence errors of players' actions to the Nash equilibrium point are linear with respect to the estimate parameters. Finally, simulations are provided to demonstrate the effectiveness of our theoretical results.

eess.SY↗
arXiv2025

Stochastic Adaptive Estimation in Polynomial Curvature Shape State Space for Continuum Robots

In continuum robotics, real-time robust shape estimation is crucial for planning and control tasks that involve physical manipulation in complex environments. In this paper, we present a novel stochastic observer-based shape estimation framework designed specifically for continuum robots. The shape state space is uniquely represented by the modal coefficients of a polynomial, enabled by leveraging polynomial curvature kinematics (PCK) to describe the curvature distribution along the arclength. Our framework processes noisy measurements from limited discrete position, orientation, or pose sensors to estimate the shape state robustly. We derive a novel noise-weighted observability matrix, providing a detailed assessment of observability variations under diverse sensor configurations. To overcome the limitations of a single model, our observer employs the Interacting Multiple Model (IMM) method, coupled with Extended Kalman Filters (EKFs), to mix polynomial curvature models of different orders. The IMM approach, rooted in Markov processes, effectively manages multiple model scenarios by dynamically adapting to different polynomial orders based on real-time model probabilities. This adaptability is key to ensuring robust shape estimation of the robot's behaviors under various conditions. Our comprehensive analysis, supported by both simulation studies and experimental validations, confirms the robustness and accuracy of our methods.

cs.RO↗