arXiv subjects

Chao Zheng

Publications and source records attributed to Chao Zheng.

At least 19 recordsLinked to original sources

arXiv2026

Error analysis and optimal sample allocation for randomized unbiased estimation: the single term estimator

Randomized unbiased estimation can be highly efficient for approximating expectations of functionals of stochastic differential equations. However, in practice, the optimal computational efficiency is rarely attained due to errors arising from parameter estimation and truncation. The effects of these errors on computational efficiency have not been investigated. In this article, we study the error analysis and optimal sample allocation for the single term estimator. Specifically, under mild assumptions on the estimated parameters, we develop a unified error analysis that incorporates both sources of error, and establish the relevant convergence rates for the computational efficiency loss. Based on this analysis, we further investigate the optimal allocation of the computational budget for parameter estimation, and propose several efficient sample allocation strategies. Numerical experiments support the theoretical convergence rates and demonstrate the effectiveness of our proposed methods.

math.ST↗

arXiv2026

Infinite rigidity of inversive distance circle packings in the Poincaré disk

The maximum principle for hyperbolic inversive distance circle packings on polyhedral surfaces is established,which unifies and generalizes existing maximum principles for various types of circle packings in the literature.As an application of this principle, a discrete Schwarz-Ahlfors lemma is established.Furthermore, an infinite rigidity theorem for weighted Delaunay triangulations of the Poincaré disk is proved,which generalizes He's hyperbolic rigidity result \cite{He2}.

math.DG↗

arXiv2026

ELISA: A Bayesian X/$γ$-ray Spectral Analysis Toolkit

Forward-folded X/$γ$-ray spectral fitting increasingly benefits from Bayesian workflows to better capture counting statistics, background uncertainties, and complex parameter degeneracies, while fully reproducible end-to-end implementations are still not always straightforward to deploy in routine analyses. We present ELISA (Efficient Library for Spectral Analysis), an open-source Python toolkit that streamlines the full spectral analysis loop for X/$γ$-ray astronomy: data loading, model construction, inference, and model validation and comparison. ELISA couples a JAX-accelerated forward model with a unified fitting interface, supporting both rapid optimization and modern Bayesian sampling. Built-in diagnostics make convergence checks and predictive assessment a standard part of the workflow, motivating principled model refinement when needed. Using a spectral fitting example of the Insight-HXMT Crab observation, we find substantial improvements in sampling efficiency compared with XSPEC's default MCMC implementation. ELISA is available at https://github.com/wcxve/elisa and on PyPI at https://pypi.org/project/astro-elisa.

astro-ph.IM↗

arXiv2026

Thurston's sphere packing on 3-dimensional manifolds, II

In this paper, we study the rigidity of Thurston's hyperbolic sphere packings on 3-dimensional manifolds. We prove that Thurston's hyperbolic sphere packing is locally determined by combinatorial scalar curvature. We further prove the infinitesimal rigidity that Thurston's hyperbolic sphere packing can not be deformed while keeping the combinatorial Ricci curvature fixed. The main tools include a characterization of the admissible space of Thurston's hyperbolic sphere packings, a variational formula for the dihedral angles of a tetrahedron and an identity for the variations of dihedral angles in a tetrahedron generated by Thurston's hyperbolic sphere packings.

math.GT↗

arXiv2026

Lightweight Soft X-ray Imager (LSXI) with glass-based coded mask

The coded mask technique has been widely used in X-ray and Gamma-ray imagers, especially in space astronomy. However, the traditional design of coded mask imagers usually has problems with large size and heavy weight. Here, we propose a novel design for a coded mask imager made of glass based on microchannel plate (MCP) technology, making it lightweight (within 1 kg), compact, and self-supporting, which is very suitable for space exploration satellites. The design is initially demonstrated by reconstructing encoded patterns from measurements at the X-ray beamline. Monte Carlo simulations of the module integrated into a 6U CubeSat are conducted to assess its in-orbit detector performance, including sensitivity and source localization.

astro-ph.IM↗

arXiv2026

Thresholdless dynamical instability in transverse $\mathcal{PT}$-symmetric scattering systems: The hidden role of bound states in the continuum

The stationary scattering properties of transverse parity-time ($\mathcal{PT}$) symmetric systems have been extensively studied, yet their dynamical stability, a prerequisite for any stationary description, remains largely unexplored. Here we uncover a thresholdless dynamical instability in such systems, driven by symmetry-protected bound states in the continuum (BICs). Without gain and loss, the up-down mirror symmetry of the transverse geometry generically protects a BIC, which manifests as the coalescence of an $S$-matrix pole and zero on the real axis of the complex wave-number plane. A Hermitian symmetry-breaking perturbation shifts the poles into the lower half-plane, converting the BIC into a resonance with a Fermi-golden-rule decay width. An anti-Hermitian $\mathcal{PT}$-symmetric perturbation instead reverses the sign of the second-order energy shift, driving the poles into the upper half-plane and producing a time-growing bound state with $\operatorname{Im}E=γ^{2}Γ_{V}/2+O(γ^{3})$, where $γ$ is the gain-loss strength and $Γ_{V}$ is the golden-rule coupling of the BIC to the continuum. The instability therefore sets in at arbitrarily small $γ$ whenever $Γ_{V}>0$. We confirm this mechanism in a three-site side-coupled model that is unstable despite possessing a unitary scattering matrix, and in a four-site rhombic model where transverse and longitudinal gain-loss placements yield vanishing and finite thresholds, respectively. These results establish a microscopic stability criterion and a design principle for stable $\mathcal{PT}$-symmetric scattering devices.

quant-ph↗

arXiv2026

Agri-Sim: Agricultural Simulation Platform for Embodied Intelligence Evaluation in Greenhouse Robotics

Agricultural-robot development requires simulation environments that can jointly support realistic scene construction, virtual sensing, autonomous navigation, motion planning, and manipulation-task execution. This paper presents Agri-Sim, a Unity and ROS2-based simulation platform for the closed-loop development and functional evaluation of agricultural robots. The platform contains a configurable tomato-greenhouse environment, a mobile dual-arm harvesting robot, virtual RGB-D, LiDAR, IMU, and joint sensors, and a bidirectional communication interface between Unity and ROS2. Unity is responsible for scene rendering, rigid-body dynamics, collision detection, virtual sensing, and task-state execution, whereas ROS2 and MoveIt 2 provide localization, navigation, collision-aware motion planning, inverse kinematics, and trajectory generation. Autonomous greenhouse navigation and dual-arm tomato harvesting were used to evaluate the complete simulation workflow. The experiments covered virtual sensor publication, ROS2-based navigation, collision-aware motion planning, mobile-base control, tomato acquisition, inter-arm handover, and box placement. The results demonstrate that Agri-Sim supports closed-loop integration and repeatable functional evaluation of navigation and manipulation workflows in a controlled virtual greenhouse, providing a practical foundation for subsequent algorithm development and Sim-to-Real studies.

cs.RO↗

arXiv2026

Minutes-long soft X-ray prompt emission from a compact object merger

Compact object mergers are multi-messenger sources and known progenitors of some gamma-ray bursts, bright flashes of high-energy radiation powered by a central engine, either an accreting black hole or a neutron star. Our understanding of these events has so far been shaped primarily by observations in the gamma-ray band, leaving their prompt phase poorly constrained at lower energies. A long-lasting ($\approx$100 s) engine-driven X-ray emission was discussed to explain rapidly fading X-ray afterglows following several ($\approx$30%) bursts of short ($\lesssim$2 s) duration. However, this prompt X-ray component was not directly observed and past candidates were not confirmed. Here we report the discovery of EP250704a containing a minutes-long ($\sim$560 s) flash of soft (0.5--4 keV) X-rays immediately following the short ($\sim$0.4 s) GRB 250704B. The variability and spectral shape of this emission are inconsistent with the canonical picture of a hard, accretion-powered spike followed by a standard external-shock afterglow. Instead, the long-soft bump points to a distinct phase of prompt emission in X-rays, which would not have been detected without the soft X-ray coverage of Einstein Probe. The detection of a prompt soft X-ray counterpart in an otherwise ordinary short GRB shows that long-lasting X-ray emission is likely a common feature of merger-driven bursts and a promising electromagnetic counterpart to gravitational wave sources.

astro-ph.HE↗

arXiv2026

Evidence of self-organized criticality in the prompt emission of a bright gamma-ray burst

Gamma-ray bursts (GRBs) are the most energetic explosive events in the Universe, yet the physical mechanism of their prompt emission remains a mystery. Especially, it is unclear whether the energy dissipation mechanism in the GRB jet is dominated by kinetic energy or magnetic energy. Here, we studied the pulses in the prompt emission of the second brightest GRB to date, GRB 230307A, which was accurately measured by the Gravitational wave high-energy electromagnetic counterpart all-sky monitor (GECAM), with focus on the cumulative distributions of peak counts and duration of pulses as well as the waiting time between pulses. We find that these cumulative distributions show scale-invariant behavior, well consistent with the prediction of the self-organized criticality (SOC) theory. This is the first robust evidence of an SOC feature in the prompt emission of a single GRB. Moreover, the statistical properties of pulses in the prompt emission of GRB 230307A are very similar to those of solar flares. Our findings suggest that the prompt emission of GRB is powered by the dissipation of magnetic energy in the ultra-relativistic jet, supporting the Poynting-flux-dominated prompt models.

astro-ph.HE↗

arXiv2026

Reasoning to Regulate: Chain-of-Thought for Traffic Rule Understanding

Understanding and complying with traffic regulations is a safety-critical requirement for autonomous driving, yet remains challenging due to the diversity and context dependence of traffic signage. Importantly, regulation understanding is not a simple recognition task, but a reasoning problem: whether a rule applies depends on interpreting the sign in relation to the spatial layout of lanes and scene context. To support such reasoning, MapDR provide fine-grained annotations that link each traffic sign's regulatory rules to the specific lanes they govern. Existing methods, however, largely treat this as direct sequence prediction, ignoring the underlying reasoning that connects sign semantics and map structure. To address this limitation, we explicitly incorporate reasoning into this task and propose a framework that equips vision-language models (VLMs) with chain-of-thought (CoT) capabilities. We first design a scalable CoT curation pipeline that bootstraps rationales from a strong LLM through a two-round strategy and employs a VLM-based verifier to filter out incorrect cases, yielding a high-quality set of (CoT, answer) pairs. Building on this foundation, we adopt a two-stage training scheme: supervised fine-tuning (SFT) to teach rationale-to-answer generation, followed by GRPO reinforcement learning with answer-grounded, fine-grained rewards to further improve final answer accuracy. Extensive experiments on MapDR show that our approach significantly improves both interpretability and accuracy, establishing the first reasoning-based framework for regulation-aware autonomous driving.

cs.CV↗

arXiv2026

Geometry-Guided Representations for Coherent Lane and Traffic Topology Reasoning in Driving Scenes

Road topology reasoning is fundamental for autonomous driving, requiring both accurate perception of road elements and understanding of their complex connectivity, including lane connectivity (Lane-to-Lane, L2L) and traffic regulation (Lane-to-Traffic signs, L2T). However, existing methods typically treat perception and topology reasoning as fragmented tasks, ignoring their potential for mutual enhancement. Crucially, while topology is inherently relational, prior works often overlook geometric relationships during feature extraction, relying instead on brittle post-processing or coordinate-based heuristics applied only at inference time. To bridge this gap, we propose CoPo (Coherent Perception and toPology), a unified framework that integrates geometry-guided relational modeling across three levels: 1) Perception-level: We introduce a relation-aware lane detector that utilizes geometry-biased self-attention and curve-guided cross-attention to enrich lane representations with structural priors; 2) Reasoning-level: We design relation-enhanced topology heads, including a geometry-enhanced L2L head and a cross-view L2T head, which effectively align features to infer connectivity; and 3) Supervision-level: We implement a contrastive InfoNCE strategy to regularize relational embeddings, pulling connected pairs closer in the latent space. This coherent multi-level design enables end-to-end joint optimization of perception and reasoning. Extensive experiments on OpenLane-V2 demonstrate that CoPo significantly outperforms existing methods, achieving gains of {\textbf{+3.1}} in DET$_l$, {\textbf{+5.3}} in TOP$_{ll}$, {\textbf{+4.9}} in TOP$_{lt}$, and {\textbf{+4.4}} overall in OLS, setting a new state-of-the-art.

cs.CV↗

arXiv2026

Optimal Spatial Anomaly Detection

There has been a growing interest in anomaly detection problems recently, whilst their focuses are mostly on anomalies taking place on the time index. In this work, we investigate a new anomaly-in-mean problem in multidimensional spatial lattice, that is, to detect the number and locations of anomaly ``spatial regions'' from the baseline. In addition to the classic minimization over the cost function with a $L_0$ penalization, we introduce an innovative penalty on the area of the minimum convex hull that covers the anomaly regions. We show that the proposed method yields a consistent estimation of the number and locations of spatial anomalies. Under the minimax framework, we characterize the optimal detection error for multidimensional spatial anomaly detection problem and reveal the trade-off between detection performance and the geometric flexibility of anomaly region shapes. Large-scale Monte Carlo simulations are carried out to examine the numeric performance of the method. The method has a wide range of applications in real-world problems. As an example, we apply it to detect the marine heatwaves using the sea surface temperature data from the European Space Agency.

stat.ME↗

arXiv2026

Trend-Aware Multi-Task Learning for Short-Term Energy Forecasting

Short-term energy forecasting plays an important role in real-time operational decision-making, such as electricity market bidding and power system dispatch, where both numerical accuracy and correct directional signals are essential. However, most existing forecasting approaches formulate the problem purely as a regression task, limiting their ability to explicitly capture stepwise directional movements and trend consistency required for operational decisions. To address this limitation, this paper proposes a trend-aware multi-task forecasting framework that decomposes forecasting outputs into directional movements and deviation magnitudes relative to the latest observation, enabling both accurate numerical prediction and interpretable trend-aware outputs. The framework adopts a task-specific dual-stream architecture and explores key design choices for integrating trend and deviation information, including hard versus probabilistic trend representations, symmetric versus asymmetric deviation modelling, and parallel versus sequential conditioning strategies. To stabilize multi-task learning and reduce manual tuning, an uncertainty-aware task weighting scheme is incorporated to automatically balance directional classification, deviation regression, and final output prediction during training. Experimental results on real-world energy datasets demonstrate that the proposed framework achieves competitive numerical accuracy compared with state-of-the-art algorithms, while consistently improving trend prediction performance with moderate computational cost. This capability is particularly beneficial in short-term energy system management, where consistent directional forecasting can provide more reliable decision support for practical operational scenarios such as market bidding, resource scheduling, and risk-aware energy management.

cs.LG↗

arXiv2026

Size-dependent dynamical instability of periodic $\mathcal{PT}$-symmetric scattering systems

While periodic $\mathcal{PT}$-symmetric structures offer a versatile platform for wave tailoring, their scattering responses are typically analyzed using stationary methods that presume dynamical stability. This assumption fails when time-growing bound states emerge, signaling a dynamical instability. Here, we analytically derive the instability threshold for a $\mathcal{PT}$-symmetric chain of $N$ unit cells with gain/loss strength $γ$. Our $S$-matrix analysis yields a closed-form threshold, $γ_c = 2\sin[π/(4N)]$, which scales as $\mathcal{O}(1/N)$ and vanishes in the thermodynamic limit. Consequently, enlarging such structures to access richer stationary band phenomena paradoxically triggers instability at weaker gain/loss. As confirmed by time-domain simulations, exceeding $γ_c$ causes exponentially growing bound states to overwhelm the system, rendering standard Bloch-wave descriptions physically irrelevant. Evaluated against this size-dependent threshold, many hallmarks of large $\mathcal{PT}$-symmetric structures, including gain-loss-induced localization, reflectionless transport, and coherent perfect absorbers and lasers, are found to lie within the dynamically unstable regime. Our findings thus establish that physical transport in non-Hermitian periodic systems is governed by a fundamental interplay between stationary band theory and finite-size stability limits.

quant-ph↗

arXiv2026

GRB 250706B/C: Insight-HXMT Discovery of a High-Luminosity Burst as a Candidate for Fallback-Regulated Accretion in the Prompt Emission

Fallback accretion in collapsar models is often associated with underluminous gamma-ray bursts (GRBs), leading to the widespread view that fallback-fed engines may be intrinsically inefficient at producing high-luminosity events. In this Letter, we present GRB 250706B/C, a luminous long GRB observed by \textit{Insight}-HXMT that exhibits an unusual combination of extreme short-timescale variability and coherent large-scale temporal evolution. The prompt emission contains at least 79 resolved pulses and a minimum variability timescale of $\sim11$ ms. The pulse widths are nearly independent of photon energy and span a broad distribution with a median FWHM of $\sim0.30$ s, while the waiting times between adjacent pulses have a median of $\sim0.38$ s. The prompt-emission envelope exhibits a prolonged rise described by $F(t)\propto (t-t_0)^{0.47\pm0.01}$ followed by a rapid decline. Despite substantial pulse-to-pulse fluctuations, neither the pulse widths nor the waiting times show significant secular evolution during the main emission episode. These features indicate the coexistence of two distinct temporal components, including a slow evolving rising luminosity envelope and rapid stochastic variability. Such behavior is consistent with scenarios in which a time-dependent engine-feeding history regulates the large-scale emission while internal dissipation within the relativistic outflow produces the pulse structure. Within this context, GRB~250706B/C may represent a fallback-fed collapsar operating on a high-luminosity branch, suggesting that fallback itself does not necessarily limit the luminosity scale of GRBs.

astro-ph.HE↗

arXiv2026

A Resource-Efficient Variational Quantum Framework for the Traveling Salesman Problem

The Traveling Salesman Problem (TSP) is a prototypical combinatorial optimization problem, but its quantum implementation is limited by the O(n^2)-qubit overhead of standard one-hot encodings. Here, we propose a resource-efficient variational quantum framework based on compact binary-register encoding, a permutation-preserving problem-inspired ansatz, and a complementary divide-and-conquer execution strategy. The compact encoding reduces the data-qubit requirement to O(n log n), while the divide-and-conquer formulation lowers the number of qubits required in each local hardware execution to the size of the largest subsystem. Numerical simulations on TSP instances with 4, 5, and 6 cities achieve best average success rates of 100%, 100%, and 95.5%, respectively. A local two-qubit implementation of the divide-and-conquer approximation is further evaluated for a 5-city TSP instance on SpinQ Gemini Pro and SpinQ Triangulum II NMR quantum computers. Taken together, the results indicate how compact encoding and divide-and-conquer execution with classical post-processing can be used to study small combinatorial optimization instances on resource-constrained quantum hardware.

quant-ph↗

arXiv2026

Joint Observation of SGR J1935+2154 with \textit{Insight}-HXMT and KM40m during the active episode of October 2022

SGR J1935+2154 is the unique magnetar so far from which fast radio bursts have been detected. In October 2022, it resumed its burst activity, and we implemented a dedicated target-of-opportunity (ToO) observation on it from Oct. 13th to Nov. 1st, 2022 (about 940 ks in total) with \textit{Insight}-HXMT, while the KM40m radio telescope observed this source for about 1400 hours since Oct. 15th. We searched the LE, ME, and HE data of \textit{Insight}-HXMT in the overlapping observation time windows with the KM40m radio telescope and revealed 60 magnetar X-ray bursts (MXBs), while KM40m only detected 1 radio burst. In particular, we find that there is an X-ray burst on October 21 (denoted as MXB 221021) temporally associated with this radio burst. Interestingly, this association event shows very different morphology from those X-ray and radio association events from this source reported before (e.g., MXB/FRB 200428). Moreover, we systematically analyzed the temporal and spectral properties of the sample of MXBs during this observation and found that % the (radio-associated) MXB 221021 shows some different properties from other MXBs without associated radio bursts. These findings shed new light on the physical mechanisms of X-ray bursts and radio burst emission in magnetars.

astro-ph.HE↗

arXiv2026

The Calabi flow with prescribed curvature on finite graphs

In this paper, we investigate the prescribed curvature problem associated with a special Lin-Lu-Yau curvature on finite graphs of girth at least 6. We define the corresponding Calabi flow for this curvature type, and establish an equivalent characterization of the problem, namely, the solution to the Calabi flow exists globally in time and converges if and only if there exists a weight function that realizes the prescribed curvature. In particular, for constant curvature weights, we prove that the solution to the Calabi flow exists globally in time and converges under certain topological conditions.

math.DG↗