arXiv subjects

Kan Chen

Publications and source records attributed to Kan Chen.

At least 19 recordsLinked to original sources

arXiv2026

Dust Substructures and Line Perturbations driven by a Forming Planet in J16120

Hints of planet formation have been independently reported within the gap of the disc around 2MASS J16120668-301027 from millimetre continuum, infrared, and H-alpha observations. In this work, we present new evidence for ongoing planet formation based on Atacama Large Millimeter/submillimeter Array (ALMA) Band 7 observations, detecting 0.87 mm dust continuum emission together with 12CO (J=3-2) and 13CO (J=3-2) line emission. Visibility modelling of the continuum data reveals an inner disc and two dust rings peaking at 23 and 75 au. The continuum morphology is better reproduced by an eccentric disc model (e approximately 0.1) than by an axisymmetric disc. We further investigate the gas kinematics through modelling of the 12CO channel maps. The residual line-width map shows a localised increase in velocity dispersion at the position of a previously reported circumplanetary disc candidate (deprojected radius approximately 32 au, position angle approximately 170 degrees) and along its orbit. This signal is spatially coincident with kink-like features and a transition from sub-Keplerian to super-Keplerian velocities. In addition, the velocity residual map exhibits an arc-like structure extending outward from the planet candidate, while the gas kinematics, despite substantial uncertainties, is consistent with inflow towards the candidate's orbital radius. The observed increase in velocity dispersion agrees with predictions from planet-disc interaction simulations, which produce enhanced turbulence both at the planet location and along its orbital path. Taken together, the continuum morphology and gas kinematic signatures provide compelling new evidence for ongoing planet formation within the disc gap.

astro-ph.EP↗

arXiv2026

Classifying the hidden-charm pentaquarks via a flavor mixing scheme

In this work, we propose a scheme to classify the molecular states consisting of ground single-charm baryons ($\Lambda_c$, $\Xi_c$, $\Sigma_c^{(*)}$, $\Xi_c^{\prime(*)}$, $\Omega_c^{(*)}$) and $\bar{D}^{(*)}/\bar{D}_s^{(*)}$ mesons. Within this framework, all considered baryon-meson systems are categorized according to the flavor components of their light degrees of freedom. We briefly illustrate how this classification scheme can consistently explain the experimentally observed $P_c$ and $P_{cs}$ states. This framework also predicts the existences of single-strange and double-strange hidden-charm bound states. The attractive interactions of these states arise from channel mixing between $\Sigma_c^{(*)}\bar{D}_s^{(*)}$ and $\Xi_c^{\prime(*)}\bar{D}^{(*)}$ for single-strange systems, and mixing between $\Xi_c^{\prime(*)}\bar{D}_s^{(*)}$ and $\Omega_c^{(*)}\bar{D}^{(*)}$ for double-strange systems, respectively. Using parameters fitted from the measured $P_c$ and $P_{cs}$ states, we systematically present the predicted mass spectra for these single- and double-strange hidden-charm bound states.

hep-ph↗

arXiv2026

Self-Organization to the Edge of Ergodicity Breaking in a Complex Adaptive System

Self-organized criticality (SOC) is widely proposed as a fundamental mechanism for collective behavior, yet its role in objective-driven, heterogeneous adaptive systems underpinning real complex systems remains less understood. We introduce EvoSK, a minimal evolutionary model in which agents perform memory dependent reinforcement learning on a rugged Sherrington-Kirkpatrick landscape while the population evolves through extremal replacement of the least fit agents. We demonstrate that this coupled dynamics drives the system to a critical state residing on the transition boundary between ergodic and non-ergodic phases. At this boundary, the system exhibits scale-free evolutionary avalanches with a mean-field exponent $\tau \approx -1.5$, while simultaneously achieving collective rewards that surpass those of any manually finetuned, non-evolutionary regime. Our results provide a mechanistic link between the statistical physics of ergodicity breaking and the functional optimality of complex adaptive systems, suggesting that the edge of ergodicity breaking acts as a robust attractor for systems adapting on rugged, high-dimensional landscapes.

nlin.AO↗

arXiv2025

Transformational astrophysics and exoplanet science with Habitable Worlds Observatory's High Resolution Imager

Habitable Worlds Observatory (HWO) will be NASA's flagship space telescope of the 2040s, designed to search for life on other planets and to transform broad areas of astrophysics. NASA are seeking international partners, and the UK is well-placed to lead the design and construction of its imaging camera - which is likely to produce the mission's most visible public impact. Early participation in the mission would return investment to UK industry, and bring generational leadership for the UK in space science, space technology, and astrophysics.

astro-ph.IM↗

arXiv2025

Task-Oriented Communications for 3D Scene Representation: Balancing Timeliness and Fidelity

Real-time Three-dimensional (3D) scene representation is a foundational element that supports a broad spectrum of cutting-edge applications, including digital manufacturing, Virtual, Augmented, and Mixed Reality (VR/AR/MR), and the emerging metaverse. Despite advancements in real-time communication and computing, achieving a balance between timeliness and fidelity in 3D scene representation remains a challenge. This work investigates a wireless network where multiple homogeneous mobile robots, equipped with cameras, capture an environment and transmit images to an edge server over channels for 3D representation. We propose a contextual-bandit Proximal Policy Optimization (PPO) framework incorporating both Age of Information (AoI) and semantic information to optimize image selection for representation, balancing data freshness and representation quality. Two policies -- the $\omega$-threshold and $\omega$-wait policies -- together with two benchmark methods are evaluated, timeliness embedding and weighted sum, on standard datasets and baseline 3D scene representation models. Experimental results demonstrate improved representation fidelity while maintaining low latency, offering insight into the model's decision-making process. This work advances real-time 3D scene representation by optimizing the trade-off between timeliness and fidelity in dynamic environments.

cs.CV↗

arXiv2025

Task-Oriented Edge-Assisted Cross-System Design for Real-Time Human-Robot Interaction in Industrial Metaverse

Real-time human-device interaction in industrial Metaverse faces challenges such as high computational load, limited bandwidth, and strict latency. This paper proposes a task-oriented edge-assisted cross-system framework using digital twins (DTs) to enable responsive interactions. By predicting operator motions, the system supports: 1) proactive Metaverse rendering for visual feedback, and 2) preemptive control of remote devices. The DTs are decoupled into two virtual functions-visual display and robotic control-optimizing both performance and adaptability. To enhance generalizability, we introduce the Human-In-The-Loop Model-Agnostic Meta-Learning (HITL-MAML) algorithm, which dynamically adjusts prediction horizons. Evaluation on two tasks demonstrates the framework's effectiveness: in a Trajectory-Based Drawing Control task, it reduces weighted RMSE from 0.0712 m to 0.0101 m; in a real-time 3D scene representation task for nuclear decommissioning, it achieves a PSNR of 22.11, SSIM of 0.8729, and LPIPS of 0.1298. These results show the framework's capability to ensure spatial precision and visual fidelity in real-time, high-risk industrial environments.

cs.RO↗

arXiv2025

HFX: Joint Design of Algorithms and Systems for Multi-SLO Serving and Fast Scaling

Large language model (LLM) serving faces the dual challenge of meeting strict user-specific service-level objectives (SLOs) while minimizing computational cost under dynamic, multi-task workloads. Existing approaches either rely on static scheduling policies or focus on single-task settings, limiting their applicability in real-world deployments with heterogeneous requests, variable prompt lengths, and elastic scaling requirements. We present HFX, a production LLM serving system that jointly optimizes request scheduling and elastic scaling across model replicas to satisfy diverse SLOs. HFX introduces a \textbf{scheduler} that performs proactive budget estimation and prioritization to ensure SLO compliance for both new and in-flight requests. HFX also integrates a \textbf{scaler} that supports fast device-to-device (D2D) weight transfer, reducing cold-start latency. Additionally, the system supports both colocated and disaggregated prefill/decode deployments, enabling adaptation to diverse workload patterns and cloud environments. Through extensive experiments on multi-task workloads, we demonstrate consistently higher SLO attainment, lower end-to-end latency, and lower NPU usage cost by up to 4.44$\times$, 65.82\%, and 49.81\%, respectively, compared to state-of-the-art systems. Our results highlight the effectiveness of SLO-aware scheduling and scaling in practical LLM serving, providing a robust framework for cost-efficient and SLO-compliant deployments.

cs.DC↗

arXiv2025

$D$-wave dimeson resonance interpretation to the $Z_c/Z_{cs}/Z_b$ states

In this work, we construct the $S$-wave, $P$-wave, and $D$-wave $D^{(*)}\bar{D}^{*}_{(s)}$/$B^{(*)}\bar{B}^{*}_{(s)}$ interactions by considering the lowest order contributions from the leading order (LO), next-to-leading order (NLO), and next-to-next-to-next-to-leading order (N$^3$LO) contact terms, respectively. After solving the corresponding Lippmann-Schwinger equation (LSE), we obtain the typical pole trajectory for the $S$-wave/$P$-wave/$D$-wave state. We adjust the $P$-wave low energy constant (LEC) and {accordingly obtain a $P$-wave $D\bar{D}^*/D^*\bar{D}$} resonance state that may correspond to the observed $G(3900)$, { and its $P$-wave $D^*\bar{D}^*$ and $B^{(*)}\bar{B}^{*}$ partners are also predicted}. Then we proceed to adjust the $D$-wave LECs to looking for the $D$-wave $D^{(*)}\bar{D}^{(*)}_{(s)}$/$B^{(*)}\bar{B}^{(*)}_{(s)}$ resonances. The satisfactory consistencies between our results { and the experimental resonance parameters} of $Z_c(3900)$/$Z_c(4020)$, $Z_b(10610)$/$Z_b(10650)$, and $Z_{cs}(4000)$/$Z_{cs}(4220)$ indicate that the $D$-wave resonance interpretation may provide a unified picture to understand these { isovector charmoniumlike states. Especially, the broad width features of $Z_{cs}(4000)$ and $Z_{cs}(4220)$ can be naturally reproduced by interpreting them as $D$-wave $D\bar{D}_s^*$ and $D^*\bar{D}_s^*$ resonances, respectively.

hep-ph↗

arXiv2025

Planet-induced Gas and Dust Substructure Feedbacks on Disk Thermal Structure

Protoplanets can interact with their natal disks and generate gas and dust substructures such as gaps and rings. However, how these planet-induced substructures affect the disk temperature, and how that in turn influences the substructures, remains unclear. We aim to study disk substructures and the thermal structure self-consistently and explore their impact on volatile distribution. To this end, we perform iterative multi-fluid hydrodynamical and radiative transfer simulations of planet-disk interactions. We find that the temperature in a structured disk deviates significantly from that of a smooth disk due to giant planet formation. In particular, midplane temperatures in gaps can increase by tens of Kelvin, leading to volatile sublimation as well as radial shifts and multiplication of icelines. Comparing our multi-dust models with previous gas-only models, we find that the former produces slightly shallower gaps and temperatures about 10 K ($\sim25\%$) higher. Furthermore, the temperature at dust rings formed by pressure bumps can drop by several Kelvin, creating volatile freeze-out regions. Nevertheless, the overall midplane ice distribution is not strongly sensitive to whether dust is included. We also investigate the effect of varying disk viscosity. Increasing $\alpha$ viscosity from $10^{-4}$ to $10^{-2}$ leads to a roughly 10 K ($\sim25\%$) warmer midplane due to enhanced vertical dust mixing. However, higher viscosity suppresses gap opening and reduces the temperature enhancement within gaps. As a result, iceline locations do not follow a simple trend with viscosity. Finally, we propose an observational strategy using ALMA to test our predicted temperature changes within disk gaps.

astro-ph.EP↗

arXiv2025

Haptic-Based User Authentication for Tele-robotic System

Tele-operated robots rely on real-time user behavior mapping for remote tasks, but ensuring secure authentication remains a challenge. Traditional methods, such as passwords and static biometrics, are vulnerable to spoofing and replay attacks, particularly in high-stakes, continuous interactions. This paper presents a novel anti-spoofing and anti-replay authentication approach that leverages distinctive user behavioral features extracted from haptic feedback during human-robot interactions. To evaluate our authentication approach, we collected a time-series force feedback dataset from 15 participants performing seven distinct tasks. We then developed a transformer-based deep learning model to extract temporal features from the haptic signals. By analyzing user-specific force dynamics, our method achieves over 90 percent accuracy in both user identification and task classification, demonstrating its potential for enhancing access control and identity assurance in tele-robotic systems.

cs.RO↗

arXiv2025

Design-Life Levels for Environmental Extremes: A Dependence-Aware Block-Maxima Workflow for Severity and Persistence

Environmental risk assessment often asks how large the maximum discharge, flood, or insured loss may become over a design life rather than in a single year. In environmental records, planning-horizon risk is complicated by limited record length, extremal clustering, and sub-asymptotic behavior, yet severity estimation, clustering assessment, and design-life levels are often handled separately. We develop a dependence-aware block-maxima workflow that links these tasks within a single inferential scheme. The severity branch estimates the extreme value index from sliding block-maximum quantile scaling using data-adaptive plateau selection and covariance-aware feasible generalized least squares. The persistence branch pools native block-maxima extremal-index paths over a stable block-size window to characterize extremal clustering. Design-life levels are then derived on the chosen observation clock, with the extremal index retained as a complementary descriptor of persistence. In synthetic short-record benchmarks, the main gain is improved interval calibration under overlap dependence, especially within block-maxima comparisons. Applications to Texas and Florida streamflow and National Flood Insurance Program building-payout claims show persistent hydrologic extremes but much faster escalation of insured losses across adjacent parts of the flood-risk chain. The workflow provides calibrated severity, persistence, and design-life levels for environmental design and flood-risk assessment under dependent records.

stat.ME↗

arXiv2025

Vine Copulas as Differentiable Computational Graphs

Vine copulas are sophisticated models for multivariate distributions and are increasingly used in machine learning. To facilitate their integration into modern ML pipelines, we introduce the vine computational graph, a DAG that abstracts the multilevel vine structure and associated computations. On this foundation, we devise new algorithms for conditional sampling, efficient sampling-order scheduling, and constructing vine structures for customized conditioning variables. We implement these ideas in torchvinecopulib, a GPU-accelerated Python library built upon PyTorch, delivering improved scalability for fitting, sampling, and density evaluation. Our experiments illustrate how gradient flowing through the vine can improve Vine Copula Autoencoders and that incorporating vines for uncertainty quantification in deep learning can outperform MC-dropout, deep ensembles, and Bayesian Neural Networks in sharpness, calibration, and runtime. By recasting vine copula models as computational graphs, our work connects classical dependence modeling with modern deep-learning toolchains and facilitates the integration of state-of-the-art copula methods in modern machine learning pipelines.

cs.LG↗

arXiv2025

Preference-Driven Active 3D Scene Representation for Robotic Inspection in Nuclear Decommissioning

Active 3D scene representation is pivotal in modern robotics applications, including remote inspection, manipulation, and telepresence. Traditional methods primarily optimize geometric fidelity or rendering accuracy, but often overlook operator-specific objectives, such as safety-critical coverage or task-driven viewpoints. This limitation leads to suboptimal viewpoint selection, particularly in constrained environments such as nuclear decommissioning. To bridge this gap, we introduce a novel framework that integrates expert operator preferences into the active 3D scene representation pipeline. Specifically, we employ Reinforcement Learning from Human Feedback (RLHF) to guide robotic path planning, reshaping the reward function based on expert input. To capture operator-specific priorities, we conduct interactive choice experiments that evaluate user preferences in 3D scene representation. We validate our framework using a UR3e robotic arm for reactor tile inspection in a nuclear decommissioning scenario. Compared to baseline methods, our approach enhances scene representation while optimizing trajectory efficiency. The RLHF-based policy consistently outperforms random selection, prioritizing task-critical details. By unifying explicit 3D geometric modeling with implicit human-in-the-loop optimization, this work establishes a foundation for adaptive, safety-critical robotic perception systems, paving the way for enhanced automation in nuclear decommissioning, remote maintenance, and other high-risk environments.

cs.RO↗

arXiv2024

Mind the kinematics simulation of planet-disk interactions: time evolution and numerical resolution

Planet-disk interactions can produce kinematic signatures in protoplanetary disks. While recent observations have detected non-Keplerian gas motions in disks, their origins are still being debated. To explore this, we conduct 3D hydrodynamic simulations using the code FARGO3D to study non-axisymmetric kinematic perturbations at 2 scale heights induced by Jovian planets in protoplanetary disks, followed by examinations of detectable signals in synthetic CO emission line observations at millimeter wavelengths. We advocate for using residual velocity or channel maps, generated by subtracting an azimuthally averaged background of the disk, to identify planet-induced kinematic perturbations. We investigate the effects of two basic simulation parameters, simulation duration and numerical resolution, on the simulation results. Our findings suggest that a short simulation (e.g., 100 orbits) is insufficient to establish a steady velocity pattern given our chosen viscosity ($\alpha=10^{-3}$), and displays plenty of fluctuations on orbital timescale. Such transient features could be detected in observations. By contrast, a long simulation (e.g., 1,000 orbits) is required to reach steady state in kinematic structures. At 1,000 orbits, the strongest and detectable velocity structures are found in the spiral wakes close to the planet. Through numerical convergence tests, we find hydrodynamics results converge in spiral regions at a resolution of 14 cells per disk scale height (CPH) or higher. Meanwhile, synthetic observations produced from hydrodynamic simulations at different resolutions are indistinguishable with 0.1$^{\prime\prime}$ angular resolution and 10 hours of integration time on ALMA.

astro-ph.EP↗

arXiv2024

Task-Oriented Edge-Assisted Cooperative Data Compression, Communications and Computing for UGV-Enhanced Warehouse Logistics

This paper explores the growing need for task-oriented communications in warehouse logistics, where traditional communication Key Performance Indicators (KPIs)-such as latency, reliability, and throughput-often do not fully meet task requirements. As the complexity of data flow management in large-scale device networks increases, there is also a pressing need for innovative cross-system designs that balance data compression, communication, and computation. To address these challenges, we propose a task-oriented, edge-assisted framework for cooperative data compression, communication, and computing in Unmanned Ground Vehicle (UGV)-enhanced warehouse logistics. In this framework, two UGVs collaborate to transport cargo, with control functions-navigation for the front UGV and following/conveyance for the rear UGV-offloaded to the edge server to accommodate their limited on-board computing resources. We develop a Deep Reinforcement Learning (DRL)-based two-stage point cloud data compression algorithm that dynamically and collaboratively adjusts compression ratios according to task requirements, significantly reducing communication overhead. System-level simulations of our UGV logistics prototype demonstrate the framework's effectiveness and its potential for swift real-world implementation.

cs.NI↗

arXiv2024

Real-Time Interactions Between Human Controllers and Remote Devices in Metaverse

Supporting real-time interactions between human controllers and remote devices remains a challenging goal in the Metaverse due to the stringent requirements on computing workload, communication throughput, and round-trip latency. In this paper, we establish a novel framework for real-time interactions through the virtual models in the Metaverse. Specifically, we jointly predict the motion of the human controller for 1) proactive rendering in the Metaverse and 2) generating control commands to the real-world remote device in advance. The virtual model is decoupled into two components for rendering and control, respectively. To dynamically adjust the prediction horizons for rendering and control, we develop a two-step human-in-the-loop continuous reinforcement learning approach and use an expert policy to improve the training efficiency. An experimental prototype is built to verify our algorithm with different communication latencies. Compared with the baseline policy without prediction, our proposed method can reduce 1) the Motion-To-Photon (MTP) latency between human motion and rendering feedback and 2) the root mean squared error (RMSE) between human motion and real-world remote devices significantly.

cs.RO↗

arXiv2024

From the $P^{N}_{\psi}$/$P^{\Lambda}_{\psi s}$ to $\bar{T}^f_{cc}$: symmetry analysis to the interactions of the $(\bar{c}q)(\bar{c}q)$/$(ccq)(\bar{c}q)$/$(ccq)(ccq)$ di-hadron systems

We investigate the interactions of the $(\bar{c}q)(\bar{c}q)$/$(ccq)(\bar{c}q)$/$(ccq)(ccq)$ di-hadron systems based on a contact lagrangian possessing the SU(3) flavor and SU(2) spin symmetries. Under the assumptions of two scenarios for the $J^P$ quantum numbers of the $P_{\psi}^N(4440)$ and $P_{\psi}^N(4457)$ states, we obtain the parameters ($\tilde{g}_s$, $\tilde{g}_a$) introduced from this contact lagrangian. Then we include the SU(3) breaking effect by introducing a factor $g_x$, this quantity can be further constrained by the experimental mass of the $P_{\psi s}^\Lambda(4338)$ state. We can reproduce the mass of the $T^f_{cc}(3875)$ state with the parameters extracted from the observed $P_{\psi}^N$ states, this consistency indicates a unified description of the di-hadron molecular states composed of two heavy-light hadrons. With the same parameters, we discuss the possible mass spectra of the $\bar{T}_{cc}^f$/$P_{\psi c}^\Lambda$/$H_{\Omega_{ccc}c}^\Lambda$ systems. Then we proceed to discuss the existences of the $\bar{T}_{cc\bar{s}}^\theta$/$P_{\psi cs}^N$/$H_{\Omega_{ccc}cs}^N$ states by investigating the SU(3) breaking effects. Our results show that the states in the $\bar{T}_{cc\bar{s}}^\theta$/$P_{\psi cs}^N$ systems can hardly form bound states, while the states in the $H_{\Omega_{ccc}cs}^N$ system can form bound states due to their larger reduced masses.

hep-ph↗

arXiv2024

Spectrum of the molecular hexaquarks

We investigate the mass spectra of molecular-type hexaquark states in the dibaryon systems. These systems are composed of the charmed baryons $[\Sigma_c^{(\ast)}$, $\Xi_c^{(\prime,\ast)}]$, doubly charmed baryons $[\Xi_{cc}^{(\ast)}]$, and hyperons $[\Sigma^{(\ast)}$, $\Xi^{(\ast)}]$. We consider all possible combinations of particle-particle and particle-antiparticle pairs, including the S-wave spin multiplets in each combination. We establish the underlying connections among the molecular tetraquarks, pentaquarks, and hexaquarks with the effective quark-level interactions. We find that the existence of molecular states in $DD^\ast$, $D\bar{D}^\ast$, and $\Sigma_c\bar{D}^{(\ast)}$ systems leads to the emergence of a large number of deuteron-like hexaquarks in the heavy flavor sectors. Currently, there have been several experimental candidates for molecular tetraquarks and pentaquarks. The experimental search for near-threshold hexaquarks will further advance the establishment of the underlying dynamical picture of hadronic molecules and deepen our understanding of the role of spin-flavor symmetry in near-threshold residual strong interactions.

hep-ph↗