arXiv subjects

Xin An

Publications and source records attributed to Xin An.

At least 19 recordsLinked to original sources

arXiv2026

Evolution of lunar wake potentials: structure, energy conversion, and their imprints on velocity distributions

We study the evolution of electric potentials in the lunar wake. The wake potential exhibits two distinct spatial scales. The macroscopic scale arises from solar wind expansion into the vacuum, with a potential length-scale growing with distance from the Moon; the microscopic scales arises from ion acoustic shocks near the wake center, with transition layers spanning tens of local Debye lengths. This two-scale potential mediates energy conversion between ions and electrons during wake refilling. The macroscale potential retards electrons and accelerates ions to supersonic velocities, converting electron thermal energy to ion kinetic energy. The microscale potential then decelerates ions to subsonic velocities and heats both species, converting ion kinetic energy back to thermal energy. Together, the two-scale potential imprints distinct signatures on velocity distributions, including ion beams and electron flat-top distributions, consistent with ARTEMIS observations.

physics.space-ph↗

arXiv2026

A fault-tolerant quantum blockchain deployed on commercial telecommunications network

Popularized by the Bitcoin cryptocurrency, blockchain technology establishes a decentralized digital framework that utilizes cryptographic and consensus protocols to secure data against unauthorized modification. Consequently, blockchain has found broad adoption across diverse fields, including finance, data management, healthcare, and digital asset governance. In the quantum computing era, a paramount objective for blockchain is to preserve its foundational advantages of cryptographic integrity and decentralized fault-tolerant resilience. In principle, quantum digital signatures and quantum Byzantine agreement protocols offer foundational security guarantees and tolerate up to one-half of malicious nodes for blockchain. However, the practical realization of such a quantum-enhanced blockchain remains a significant and multifaceted challenge. Here, we propose and experimentally demonstrate a fully operational hybrid quantum blockchain architecture built on photonic integrated circuits and deployed over commercially available classical telecommunications infrastructure. The system achieves a fault tolerance of nearly one-half, surpassing the classical limit, while reaching consensus on a timescale of seconds. A deployed food traceability application validates the practicality of the proposed architecture, achieving a throughput of approximately 500 transactions per second. This work establishes a foundation for practical quantum blockchains, enabling secure, scalable, and decentralized information processing in the emerging quantum era.

quant-ph↗

arXiv2026

Experimental demonstration of scalable quantum blockchain with exponentially superior quantum communication complexity

To secure modern distributed digital infrastructures, quantum blockchains exploit quantum resources to achieve information-theoretic security and surpass the classical one-third fault-tolerance bound. However, existing high-fault-tolerant protocols face a fundamental scalability challenge: the blockchain trilemma imposes either exponential communication complexity or experimentally demanding multipartite entanglement. Here, we experimentally demonstrate a scalable quantum blockchain protocol based on weak coherent states that achieves an exponential reduction in quantum communication complexity. The protocol employs a circular quantum Byzantine agreement mechanism that preserves information-theoretic security while avoiding multipartite entanglement. We implement this protocol on a photonic integrated circuit platform, realizing a six-node network over commercially available telecommunication infrastructure. Compared with previous schemes, the protocol requires less than 4% of the quantum communication resources. Leveraging this advantage, we further demonstrate a quantum-secured token exchange application achieving a throughput of 805.3 transactions per second with zero failures. These results establish a practical pathway toward scalable quantum blockchain.

quant-ph↗

arXiv2026

Diversity-Driven Offline Multi-Objective Optimization via Nested Pareto Set Learning

Multi-objective optimization (MOO) has emerged as a powerful approach to solving complex optimization problems involving multiple objectives. In many practical scenarios, function evaluations are unavailable or prohibitively expensive, necessitating optimization solely based on a fixed offline dataset. In this setting, known as offline MOO, the goal is to find out the Pareto set without access to the true objective functions. This setting suffers from the out-of-distribution (OOD) issue, where the surrogate model is not accurate for unseen designs. Due to the OOD issue, surrogate errors may cause the optimizer to select solutions that do not lie on the true Pareto front and are biased toward its extremes. To address this, this paper proposes Diversity-driven Offline Multi-Objective Optimization (DOMOO), which aims to find out a diverse and high-quality set of solutions. First, DOMOO incorporates an accumulative risk control module that estimates the potential risk of candidate solutions and alleviates the OOD issue between the training data and the generated solutions. In addition, a nested Pareto set learning (PSL) strategy is proposed to jointly learn preference and PSL parameters, then optimize them, enabling adaptation to diverse Pareto front geometries. To further enhance solution quality, we design a diversity-driven selection strategy that extracts a representative and well-distributed set of final solutions. To achieve this diversity-driven selection strategy, we propose $\text{IGD}_\text{offline}$, a tailored indicator for the offline setting that considers both diversity and convergence, and avoids the bias of hypervolume indicator. Extensive experiments on synthetic and real-world benchmarks show that DOMOO achieves the best average rank across tasks in both convergence and diversity among the compared methods.

cs.LG↗

arXiv2026

Cumulant dynamics in finite-memory diffusion

Fluctuations of conserved charges are among the main proposed signatures of the quantum chromodynamics (QCD) critical point, but their interpretation requires a dynamical description of how fluctuation correlators evolve during the finite lifetime of the quark--gluon plasma (QGP) fireball. The standard baseline for this evolution is Fickian diffusion, in which the diffusive current follows the local density gradient instantaneously. This instantaneous-current limit can miss delayed-response effects when the current-relaxation time becomes comparable to the relaxation time of the relevant fluctuation modes. In this work we extend this baseline to Maxwell--Cattaneo diffusion, where the current relaxes on a finite time scale and therefore retains memory. We derive closed evolution equations for multi-point Wigner functions and convert the freezeout correlators into acceptance-dependent cumulants along representative trajectories in the QCD phase diagram. While Fickian diffusion already causes the correlators to lag behind their instantaneous equilibrium values, finite current relaxation introduces an additional memory effect beyond this diffusive lag. As a result, current memory can suppress, shift, and reshape the non-monotonic behavior of the cumulants relative to both instantaneous equilibrium and Fickian diffusion, with the most visible effects appearing in higher-order cumulants and their ratios.

hep-th↗

arXiv2026

Collisionless Phase Mixing Mimics Diffusive Transport in Radiation Belt Observations

Since the dawn of the space age, observations of energetic particles in planetary radiation belts have been interpreted within a diffusive transport framework, even though the dominant processes that populate and deplete these belts-such as injections and moon-driven absorption-produce highly structured, spatially localized particle distributions. This exposes a fundamental question: how can coherent phase-space structures evolving under collisionless dynamics give rise to observational signatures consistent with diffusion-based transport? Here we show that diffusion-like behaviour inferred from radiation belt observations can arise solely from an observational phase-mixing effect, independent of stochastic wave-particle transport. As orbiting spacecraft sweep across neighbouring drift shells while trapped particles undergo electromagnetic drifts, measurements inevitably sample regions with slightly different drift frequencies. This converts localized drift-phase structures into rapidly decorrelating temporal signals, making them observationally indistinguishable from those produced by stochastic wave-particle processes. We derive the associated correlation function analytically and show that the effective lifetime of these structures is only a few drift periods. Consequently, even highly localized injections rapidly lose coherence, preventing spacecraft from resolving fine-scale structure in the distribution function. These results show that collisionless dynamics can produce observational signatures that mimic diffusive transport on timescales shorter than those expected from radial transport, biasing inferred transport rates and long-term flux predictions. This calls for a reassessment of diffusion-based interpretations from sparse in-situ measurements of radiation belts at Earth, across the solar system, and in the recently discovered radiation belts of ultra-cool brown dwarfs.

physics.plasm-ph↗

arXiv2026

Inferring lunar wake potentials from electron phase space densities

Inferring electric potentials from electron phase space density measurements in the lunar wake is complicated by two challenges: the asymmetry between the sunward and anti-sunward sides of the wake driven by the solar wind strahl, and the presence of ion acoustic shocks in the central wake. We develop the Hamiltonian inversion method, which infers the full spatial electric potential profile by exploiting the quasi-static Vlasov equilibrium condition $f = f(H)$, where $H$ is the electron Hamiltonian. The method addresses both challenges through a domain-decomposition strategy: on the two sides of the wake the potential is inferred independently by minimizing the misfit between the observed phase space density and a self-consistently reconstructed $f_\mathrm{interp}(\tilde{H})$, while in the central wake where flat-top trapped electron distributions are present the potential is inferred directly from the flat-top width. We validate the method against particle-in-cell simulation data at two evolutionary stages of the lunar wake: an early stage where strahl asymmetry is strong but no shocks have formed, and a later stage where ion acoustic shocks and flat-top distributions are present. We then apply the method to two ARTEMIS lunar wake crossings at the same evolutionary stages, inferring normalized potential drops of $e\Delta\varphi/T_e \sim 15$ and $\sim 5$ respectively and capturing shock-associated potential enhancements in the central wake. The method is broadly applicable to plasma environments where electrons are in quasi-static equilibrium with a field-aligned electric potential.

physics.space-ph↗

arXiv2026

Non-Gaussian fluctuations in relativistic hydrodynamics: Confluent equations for three-point correlations

We derive deterministic equations for the evolution of non-Gaussian fluctuations in relativistic stochastic hydrodynamics. This is achieved by defining the average local Landau frame and corresponding fluctuating hydrodynamic variables. Fully nonlinear stochastic hydrodynamics is expressed in a unified multi-component matrix form. A novel relativistic formalism, also manifestly covariant under SO(3) rotations of the local spatial basis in the average local Landau frame, is introduced. The equations describe correlators of all hydrodynamic variables, including fluctuating velocity (or momentum density) -- a nontrivial problem in relativistic hydrodynamics.

nucl-th↗

arXiv2026

Ion pickup and velocity space thermalization at outer planet moons

Ion pickup at the outer planets' active moons is a fundamental plasma process in which newly ionized particles from moon exospheres interact with the ambient corotating plasma and are accelerated to match the background flow. Spacecraft observations have revealed intense electromagnetic wave activity commonly attributed to this pickup process. Here we investigate ion pickup using hybrid-kinetic simulations in which ions are treated kinetically while electrons are modeled as a massless fluid. In the moon's rest frame, ambient ions initially stream perpendicular to the background magnetic field at the corotation velocity, creating a nongyrotropic velocity distribution with two ion populations clustered at opposite gyrophases. Within a few ion gyroperiods, this configuration simultaneously excites transverse magnetic perturbations associated with electromagnetic ion cyclotron waves and compressional perturbations associated with mirror-mode and ion Bernstein waves, reaching amplitudes of several percent of the background field strength. Using field-particle correlation analysis, we quantify the energy transfer between waves and particles and demonstrate how these perturbations scatter ions in velocity space, efficiently incorporating newly created ions into the background plasma and leading to isotropization in both gyrophase and pitch angle. These results provide a kinetic framework for understanding pickup-driven wave-particle interactions and offer guidance for interpreting in situ measurements at active moons throughout the outer solar system.

physics.space-ph↗

arXiv2026

AgentChemist: A Multi-Agent Experimental Robotic Platform Integrating Chemical Perception and Precise Control

Chemical laboratory automation has long been constrained by rigid workflows and poor adaptability to the long-tail distribution of experimental tasks. While most automated platforms perform well on a narrow set of standardized procedures, real laboratories involve diverse, infrequent, and evolving operations that fall outside predefined protocols. This mismatch prevents existing systems from generalizing to novel reaction conditions, uncommon instrument configurations, and unexpected procedural variations. We present a multi-agent robotic platform designed to address this long-tail challenge through collaborative task decomposition, dynamic scheduling, and adaptive control. The system integrates chemical perception for real-time reaction monitoring with feedback-driven execution, enabling it to adjust actions based on evolving experimental states rather than fixed scripts. Validation via acid-base titration demonstrates autonomous progress tracking, adaptive dispensing control, and reliable end-to-end experiment execution. By improving generalization across diverse laboratory scenarios, this platform provides a practical pathway toward intelligent, flexible, and scalable laboratory automation.

cs.RO↗

arXiv2026

Logics-Parsing-Omni Technical Report

Addressing the challenges of fragmented task definitions and the heterogeneity of unstructured data in multimodal parsing, this paper proposes the Omni Parsing framework. This framework establishes a Unified Taxonomy covering documents, images, and audio-visual streams, introducing a progressive parsing paradigm that bridges perception and cognition. Specifically, the framework integrates three hierarchical levels: 1) Holistic Detection, which achieves precise spatial-temporal grounding of objects or events to establish a geometric baseline for perception; 2) Fine-grained Recognition, which performs symbolization (e.g., OCR/ASR) and attribute extraction on localized objects to complete structured entity parsing; and 3) Multi-level Interpreting, which constructs a reasoning chain from local semantics to global logic. A pivotal advantage of this framework is its evidence anchoring mechanism, which enforces a strict alignment between high-level semantic descriptions and low-level facts. This enables ``evidence-based'' logical induction, transforming unstructured signals into standardized knowledge that is locatable, enumerable, and traceable. Building on this foundation, we constructed a standardized dataset and released the Logics-Parsing-Omni model, which successfully converts complex audio-visual signals into machine-readable structured knowledge. Experiments demonstrate that fine-grained perception and high-level cognition are synergistic, effectively enhancing model reliability. Furthermore, to quantitatively evaluate these capabilities, we introduce OmniParsingBench. Code, models and the benchmark are released at https://github.com/alibaba/Logics-Parsing/tree/master/Logics-Parsing-Omni.

cs.AI↗

arXiv2026

Cavitons Associated with Ion-Acoustic-Like Waves in Foreshock Transients

Foreshock transients upstream of the Earth's bow shock, such as foreshock bubbles and hot flow anomalies, are often characterized by reduced-density cores and strong plasma fluctuations. These conditions provide environments where electrostatic wave activity and localized density structures can coexist. Using high-time-resolution measurements from the Magnetospheric Multiscale (MMS) mission, we investigate the relationship between bursty electrostatic wave activity and localized electron density depletions within foreshock transients. A representative case study reveals a clear scaling between wave activity and density depletion, and a statistical analysis across multiple events shows that this scaling persists when the wave activity, with characteristics consistent with ion-acoustic-like waves, is represented in terms of electrostatic potential fluctuations normalized by electron temperature. In contrast, representations based on electric field amplitude, even when similarly normalized, exhibit substantial event-to-event variability. These results provide observational evidence for a causal relationship between ion-acoustic-like electrostatic wave activity and cavitons in foreshock plasmas.

physics.plasm-ph↗

arXiv2025

Ion Temperature Anisotropy Limits from Magnetic Curvature Scattering in Magnetotail Reconnection Jets

In collisionless plasmas, relaxation of the deviations of ion velocity distribution functions (VDFs) from local thermodynamic equilibrium occurs through particle interactions with electromagnetic fields. In particular, in the Earth's magnetotail, the deviations of the ion VDFs, typically consisting of multiple components, from the equilibrium must be limited to maintain stability of the current sheet. Curvature scattering is a leading candidate mechanism to limit such deviations, but its role remains insufficiently understood. We investigate the limits of ion temperature anisotropy in a magnetotail-like configuration by modeling a quasi-1D current sheet with a finite magnetic field curvature and three ion populations. We derive analytical thresholds for anisotropy based on current sheet stability and validate against spacecraft observations and numerical simulations. Our findings demonstrate that curvature scattering imposes limits on ion anisotropies, thereby maintaining the stability of the current sheet.

physics.plasm-ph↗

arXiv2025

Building far-from-equilibrium effective field theories using shift symmetries

Contemporary understanding of thermalization in quantum field theory stems largely from understanding properties of transient excitations of equilibria. These nonhydrodynamic excitations are known to structurally differ between weakly- and strongly-coupled quantum field theories with no known results at intermediate values of the interaction strength. We demonstrate that all the known behaviors of transient excitations can be understood as a consequence of different realizations of a symmetry principle, the shift symmetry, applied at the level of the far from equilibrium generalization of the hydrodynamic effective action that we explicitly construct. Our approach naturally includes the effects of stochastic fluctuations outside the hydrodynamic regime and allows to explicitly construct hybrid models interpolating between weak- and strong-coupling behavior. We study properties of one such model motivated by thermalization in nuclear collisions in light of the QCD running coupling.

hep-th↗

arXiv2025

Energy transfer from MHD-scale slow-mode waves to kinetic-scale ion acoustic waves

Large-amplitude slow-mode waves are commonly observed near Earth's magnetopause. Recent observations show that these waves can occur simultaneously with kinetic-scale ion acoustic waves. The amplitude of the ion acoustic waves is enhanced near the magnetic field peaks of the slow-mode wave, suggesting that the slow-mode waves may drive the generation of ion acoustic waves. To test this hypothesis, we conduct a hybrid simulation using observation-based parameters. The simulation results demonstrate that large-amplitude slow-mode waves generate counter-streaming ion beams, which in turn excite ion acoustic waves and relax the ion beams. Our study reveals a clear energy transfer channel from MHD-scale slow-mode waves to kinetic-scale ion acoustic waves.

physics.plasm-ph↗

arXiv2025

A HyperGraphMamba-Based Multichannel Adaptive Model for ncRNA Classification

Non-coding RNAs (ncRNAs) play pivotal roles in gene expression regulation and the pathogenesis of various diseases. Accurate classification of ncRNAs is essential for functional annotation and disease diagnosis. To address existing limitations in feature extraction depth and multimodal fusion, we propose HGMamba-ncRNA, a HyperGraphMamba-based multichannel adaptive model, which integrates sequence, secondary structure, and optionally available expression features of ncRNAs to enhance classification performance. Specifically, the sequence of ncRNA is modeled using a parallel Multi-scale Convolution and LSTM architecture (MKC-L) to capture both local patterns and long-range dependencies of nucleotides. The structure modality employs a multi-scale graph transformer (MSGraphTransformer) to represent the multi-level topological characteristics of ncRNA secondary structures. The expression modality utilizes a Chebyshev Polynomial-based Kolmogorov-Arnold Network (CPKAN) to effectively model and interpret high-dimensional expression profiles. Finally, by incorporating virtual nodes to facilitate efficient and comprehensive multimodal interaction, HyperGraphMamba is proposed to adaptively align and integrate multichannel heterogeneous modality features. Experiments conducted on three public datasets demonstrate that HGMamba-ncRNA consistently outperforms state-of-the-art methods in terms of accuracy and other metrics. Extensive empirical studies further confirm the model's robustness, effectiveness, and strong transferability, offering a novel and reliable strategy for complex ncRNA functional classification. Code and datasets are available at https://anonymous.4open.science/r/HGMamba-ncRNA-94D0.

cs.LG↗

arXiv2025

Codebook-Based Adaptive Feature Compression With Semantic Enhancement for Edge-Cloud Systems

Coding images for machines with minimal bitrate and strong analysis performance is key to effective edge-cloud systems. Several approaches deploy an image codec and perform analysis on the reconstructed image. Other methods compress intermediate features using entropy models and subsequently perform analysis on the decoded features. Nevertheless, these methods both perform poorly under low-bitrate conditions, as they retain many redundant details or learn over-concentrated symbol distributions. In this paper, we propose a Codebook-based Adaptive Feature Compression framework with Semantic Enhancement, named CAFC-SE. It maps continuous visual features to discrete indices with a codebook at the edge via Vector Quantization (VQ) and selectively transmits them to the cloud. The VQ operation that projects feature vectors onto the nearest visual primitives enables us to preserve more informative visual patterns under low-bitrate conditions. Hence, CAFC-SE is less vulnerable to low-bitrate conditions. Extensive experiments demonstrate the superiority of our method in terms of rate and accuracy.

cs.CV↗

arXiv2025

ARTEMIS observations of electrostatic shocks inside the lunar wake

When the solar wind encounters the Moon, a plasma void forms downstream of it, known as the lunar wake. In regions where the magnetic field is quasi-parallel to the plasma-vacuum boundary normal, plasma refills the wake primarily along magnetic field lines. As faster electrons outpace slower ions, an ambipolar electric field is generated, accelerating ions and decelerating electrons. Recent particle-in-cell simulations have shown that when accelerated supersonic ion beams from opposite sides of the wake meet near the wake center, electrostatic shocks may form, decelerating ions and heating electrons into flat-top velocity distributions. Using data from the Acceleration, Reconnection, Turbulence and Electrodynamics of the Moon's Interaction with the Sun (ARTEMIS) spacecraft, we present the first observational evidence of the predicted electrostatic shocks. Near the wake center of one event, we observed an electrostatic solitary structure with an amplitude of ~2 mV/m and a spatial scale of ~50 local Debye lengths. This structure generated a potential increase of ~50 V from upstream to downstream, heating incoming electrons by ~50 eV in the parallel direction while decelerating ions by ~60 km/s leading to a density enhancement. At a second event representing a more evolved stage, we observed more dissipated structures dominated by strong electrostatic waves, with persistent potential increases driving continued field-aligned electron heating and ion deceleration. These observations confirm simulation predictions of electrostatic shock formation and the associated particle dynamics within the lunar wake, with potential applications to understanding plasma interactions around other airless celestial bodies.

physics.space-ph↗