arXiv subjects

Yong Zhang

Publications and source records attributed to Yong Zhang.

At least 91 records · Page 5Linked to original sources

arXiv2026

Analysis of Eruptive Prominence Plasma Parameters' Effects on the \ion{He}{2} 304~Å Line with Solar Orbiter EUI Observations

An observation of a large prominence on the solar limb took place on February 15, 2022, by the Extreme Ultraviolet Imager (EUI) on board Solar Orbiter. We aim to determine the range of physical parameters of this prominence, such as temperature, radial velocity, and altitude, and examine how these parameters affect the formation of the 304~Å line of \ion{He}{2}, especially how collisional excitation and radiative processes contribute to line formation. After constraining these parameters, we generate 200 random models and compute the \ion{He}{2} 304~Å line profile. We present these results using parallel coordinate plots to explore how these parameters affect the results. This allows us to infer the key physical parameters that impact the formation of the \ion{He}{2} 304~Å line. This study demonstrates that column mass and the steepness of the temperature profile are key factors in the formation of the \ion{He}{2} 304~Å line during the solar prominence eruption on February 15, 2022. Radiative processes remain dominant in the formation of the \ion{He}{2} 304~Å line. These insights provide a foundation for future research and comparative studies.

astro-ph.SR↗

arXiv2026

Analysis of Lyman-beta and Lyman-gamma Lines in a Pre-Eruptive and Eruptive Prominence with Solar Orbiter SPICE Observations

The first dedicated observation of an off-limb prominence by the Spectral Imaging of the Coronal Environment (SPICE) instrument on board Solar Orbiter took place on April 15, 2023. Our aim is to provide an overview of the potentiality of the diagnostics using these data. We show that we can derive the changes in the physical parameters of the pre-eruptive and eruptive prominence using the Lyman lines. We investigate the integrated intensity and line widths of the Lyman $β$ and Lyman $γ$ lines, finding variations between the prominence, disk, and coronal regions. The results reflect dynamic changes in density, temperature, and optical thickness. We analyze the spatial and temporal evolution of the Lyman $β$ and Lyman $γ$ line profiles. Using a simple geometric model, we obtain the radial velocity of this prominence at the early phase of its eruption with GONG H$α$ images. This offers a way of calculating the radial velocity of an eruptive filament from a pair of 2D images. The result helps us understand the potential Doppler effect in line profiles. Overall, the spectral profiles indicate that the eruption enhances spatial and temporal variations in line intensity, reflecting dynamic changes in plasma conditions within the prominence. These findings highlight the diagnostic potential of SPICE observations, and future Non-LTE radiative transfer modeling will help to further constrain prominence plasma parameters.

astro-ph.SR↗

arXiv2026

Non-LTE Analysis of Pre-eruptive Prominence Plasma Parameters Effects on the Lyman-beta and Lyman-gamma Lines with Solar Orbiter SPICE Observations

The first dedicated observation of an off-limb prominence by Solar Orbiter took place on April 15, 2023. Our aim is to determine the range of different physical parameters of this prominence and to examine how these parameters affect the formation of the Lyman $β$ and Lyman $γ$ lines of hydrogen. We have found a way to refine key physical parameters by observational data. We will test the method by this prominence observation. We generate 200 random non-LTE models using these observational constraints and compute the Lyman $β$ line and the Lyman $γ$ line profiles. We use the Spectral Imaging of the Coronal Environment (SPICE) full-disk mosaic from November 13, 2023 to constrain the incident radiation. We present the parameters and results of 200 random models using parallel coordinate plots to explore how different parameters affect the results. This allows us to infer the key physical parameters (e.g., central pressure, column mass and temperature gradient) that impact the formation of the Lyman $β$ line and the Lyman $γ$ line in this observation.

astro-ph.SR↗

arXiv2026

TAMISeg: Text-Aligned Multi-scale Medical Image Segmentation with Semantic Encoder Distillation

Medical image segmentation remains challenging due to limited fine-grained annotations, complex anatomical structures, and image degradation from noise, low contrast, or illumination variation. We propose TAMISeg, a text-guided segmentation framework that incorporates clinical language prompts and semantic distillation as auxiliary semantic cues to enhance visual understanding and reduce reliance on pixel-level fine-grained annotations. TAMISeg integrates three core components: a consistency-aware encoder pretrained with strong perturbations for robust feature extraction, a semantic encoder distillation module with supervision from a frozen DINOv3 teacher to enhance semantic discriminability, and a scale-adaptive decoder that segments anatomical structures across different spatial scales. Experiments on the Kvasir-SEG, MosMedData+, and QaTa-COV19 datasets demonstrate that TAMISeg consistently outperforms existing uni-modal and multi-modal methods in both qualitative and quantitative evaluations. Code will be made publicly available at https://github.com/qczggaoqiang/TAMISeg.

cs.CV↗

arXiv2026

A complex-linear reformulation of Hamilton-Jacobi theory and emergent quantum structure

Classical mechanics admits multiple equivalent formulations, from Newton's equations to the variational Lagrange-Hamilton framework and the scalar Hamilton-Jacobi (HJ) theory. In the HJ formulation, classical ensembles evolve through the continuity equation for a real density $ρ= R^{2}$ coupled to Hamilton's principal function $S$. Here we develop a complementary formulation, the Hamilton-Jacobi-Schrödinger (HJS) theory, by embedding the pair $(R,S)$ into a single complex field. Starting from a completely general complex ansatz $ψ= f(R,S)\, e^{i g(R,S)},$ and imposing two minimal structural requirements, we obtain a unique map $ψ= R\, e^{iS/κ}\, $ together with a linear HJS equation whose $|κ| \to 0$ limit reproduces the HJ formulation exactly. Remarkably, when $\mathrm{Re}(κ)\neq 0$, essential features of quantum mechanics, superposition, operator algebra, commutators, the Heisenberg uncertainty principle, Born's rule and unitary evolution, follow naturally as structural consistency conditions. HJS thus provides a unified mathematical viewpoint in which classical and quantum dynamics appear as different limits of a single underlying structure.

quant-ph↗

arXiv2026

TRACE: Transparent Web Reliability Assessment with Contextual Explanations

In an era of AI-generated misinformation flooding the web, existing tools struggle to empower users with nuanced, transparent assessments of content credibility. They often default to binary (true/false) classifications without contextual justifications, leaving users vulnerable to disinformation. We address this gap by introducing TRACE: Transparent Reliability Assessment with Contextual Explanations, a unified framework that performs two key tasks: (1) it assigns a fine-grained, continuous reliability score (from 0.1 to 1.0) to web content, and (2) it generates a contextual explanation for its assessment. The core of TRACE is the TrueGL-1B model, fine-tuned on a novel, large-scale dataset of over 140,000 articles. This dataset's primary contribution is its annotation with 35 distinct continuous reliability scores, created using a Human-LLM co-creation and data poisoning paradigm. This method overcomes the limitations of binary-labeled datasets by populating the mid-ranges of reliability. In our evaluation, TrueGL-1B consistently outperforms other small-scale LLM baselines and rule-based approaches on key regression metrics, including MAE, RMSE, and R2. The model's high accuracy and interpretable justifications make trustworthy information more accessible. To foster future research, our code and model are made publicly available here: github.com/zade90/TrueGL.

cs.IR↗

arXiv2026

An efficient compact splitting Fourier spectral methods for computing the dynamics of rotating spin-orbit coupled spin-2 Bose-Einstein condenstates

This paper investigates the dynamics of spin-2 Bose-Einstein condensates (BECs) with rotation and spin-orbit coupling (SOC). In order to better simulate the dynamics, we present an efficient high-order compact splitting Fourier spectral method. This method splits the Hamiltonian into a linear part, which consists of the Laplace, rotation and SOC terms, and a nonlinear part that includes all the remaining terms. The wave function is well approximated by the Fourier spectral method and is numerically accessed with discrete Fast Fourier transform (FFT). For linear subproblem, the handling of rotation term and SOC term poses a major challenge. Using a function mapping based on rotation, we can integrate the linear subproblem exactly and explicitly. This mapping we propose not only helps eliminate the rotation term, but also prevents the SOC term from evolving into a time-dependent form. The nonlinear subproblem is integrated analytically in physical space. Such "compact" splitting involves only two operators and facilitates the design of high-order splitting schemes. Our method is spectrally accurate in space and high order in time. It is efficient, explicit, unconditionally stable and simple to implement. In addition, we derive some dynamical properties and carry out a systematic study, including accuracy and efficiency tests, dynamical property verification, the SOC effects and dynamics of vortex lattice.

math.NA↗

arXiv2026

Local asymmetry in interference as a probe of quantum probability

Quantum interference provides one of the most sensitive probes of quantum mechanics. While linear superposition fixes the positions and quadratic curvature of interference fringes, it remains unclear whether the probabilistic postulate itself, the Born rule, can be tested through finer, local features of interference patterns. Here we show that a minimal deformation of quantum probability gives rise to a robust and symmetry-protected signature: a left-right asymmetry in the local shape of interference fringes. Remarkably, this effect leaves the linear Schrödinger dynamics intact and does not shift fringe positions or modify their quadratic curvature. Instead, it appears exclusively as a cubic skewness of local intensity profiles, providing a clean and falsifiable observable. We demonstrate this behavior within a controlled realization that preserves linear dynamics while minimally deforming the probabilistic assignment. The resulting signature is universal, scale insensitive, and cannot be mimicked by conventional sources of experimental noise. Our results identify local asymmetry in interference as a direct probe of quantum probability itself, suggesting that features often regarded as removable imperfections may encode fundamental information beyond fringe positions and widths.

quant-ph↗

arXiv2026

A Candidate Open Cluster Pulsar: Timing Analysis of PSR J1922+3745 in NGC 6791

PSR J1922+3745 was recently identified as a radio pulsar toward the old open cluster NGC 6791, raising the prospect of the first pulsar associated with an open cluster. We report FAST follow-up observations that yield a phase-coherent timing solution, a precise position, a measurement of the spin-down rate and the pulsar's polarization properties. PSR J1922+3745 is consistent with an isolated slow pulsar with a characteristic age of 7.8 Myr, comparable to the small population of long-period pulsars found in globular clusters. Motivated by the potential cluster association, we re-process deeper searches of the NGC 6791 field at higher sensitivity but detect no additional pulsars. We also assess whether HI absorption spectroscopy can provide a useful distance constraint and find that such measurements are unlikely to be constraining with currently available sensitivity. Consequently, existing evidence does not yet establish membership in NGC 6791. Further deep searches for additional pulsars with similar dispersion measures in the cluster field will likely be the most direct path to confirming a physical association.

astro-ph.HE↗

arXiv2026

WildActor: Unconstrained Identity-Preserving Video Generation

Production-ready human video generation requires digital actors to maintain strictly consistent full-body identities across dynamic shots, viewpoints and motions, a setting that remains challenging for existing methods. Prior methods often suffer from face-centric behavior that neglects body-level consistency, or produce copy-paste artifacts where subjects appear rigid due to pose locking. We present Actor-18M, a large-scale human video dataset designed to capture identity consistency under unconstrained viewpoints and environments. Actor-18M comprises 1.6M videos with 18M corresponding human images, covering both arbitrary views and canonical three-view representations. Leveraging Actor-18M, we propose WildActor, a framework for any-view conditioned human video generation. We introduce an Asymmetric Identity-Preserving Attention mechanism coupled with a Viewpoint-Adaptive Monte Carlo Sampling strategy that iteratively re-weights reference conditions by marginal utility for balanced manifold coverage. Evaluated on the proposed Actor-Bench, WildActor consistently preserves body identity under diverse shot compositions, large viewpoint transitions, and substantial motions, surpassing existing methods in these challenging settings.

cs.CV↗

arXiv2026

MAViD: A Multimodal Framework for Audio-Visual Dialogue Understanding and Generation

We propose MAViD, a novel Multimodal framework for Audio-Visual Dialogue understanding and generation. Existing approaches primarily focus on non-interactive systems and are limited to producing constrained and unnatural human speech. The primary challenge of this task lies in effectively integrating understanding and generation capabilities, as well as achieving seamless multimodal audio-video fusion. To solve these problems, we propose a Conductor-Creator architecture that divides the dialogue system into two primary components. The Conductor is tasked with understanding, reasoning, and generating instructions by breaking them down into motion and speech components, thereby enabling fine-grained control over interactions. The Creator then delivers interactive responses based on these instructions. Furthermore, to address the difficulty of generating long videos with consistent identity, timbre, and tone using dual DiT structures, the Creator adopts a structure that combines autoregressive (AR) and diffusion models. The AR model is responsible for audio generation, while the diffusion model ensures high-quality video generation. Additionally, we propose a novel fusion module to enhance connections between contextually consecutive clips and modalities, enabling synchronized long-duration audio-visual content generation. Extensive experiments demonstrate that our framework can generate vivid and contextually coherent long-duration dialogue interactions and accurately interpret users' multimodal queries.

cs.CV↗

arXiv2026

An efficient and accurate numerical method for computing the ground states of three-dimensional rotating dipolar Bose-Einstein condensates under strongly anisotropic trap

In this article, we propose an efficient and spectrally accurate numerical method to compute the ground states of three-dimensional (3D) rotating dipolar Bose-Einstein condensates (BEC) under strongly anisotropic trapping potentials.The kernel singularity, convolution non-locality and density anisotropy together complicate the dipolar potential evaluation. The fast rotation mechanism not only induces a complicated energy landscape with many local minima, but also creates a large number of vortices in the condensates. Such factors collectively make the ground state computation challenging in terms of convergence, accuracy and efficiency, especially for 3D anisotropic systems. Coupled with Fourier spectral discretization, we proposed a preconditioned conjugate gradient method (PCG) by integrating the anisotropic truncated kernel method (ATKM) for the dipolar potential evaluation. An adaptive step size control strategy is designed and ATKM allows for a spectral accuracy without introducing any extra anisotropy-dependent memory requirement or computational time. Our algorithm is spectrally accurate, highly efficient and memory-economic. Extensive numerical results are presented to confirm the accuracy and efficiency, together with applications to study impacts of the model parameters on critical rotational frequency, energies and chemical potential. Furthermore, these simulations reveal additional novel ground state patterns, such as bent vortices.

math.NA↗

arXiv2026

Huawei Cloud Model-as-a-Service on the CloudMatrix384 SuperPod

Scaled-out MoE LLMs and scaled-up SuperPods create new systems challenges for production Model-as-a-Service (MaaS), requiring disaggregation, low-latency communication, and decentralized serving. This report presents xDeepServe, the production serving system behind Huawei Cloud's MaaS offering on CloudMatrix384, a 48-server SuperPod with 384 Ascend 910C chips connected by a high-bandwidth UB fabric and global shared memory. It serves models including DeepSeek, Kimi, GLM, Qwen, and MiniMax, among others. xDeepServe is built around Transformerless, a disaggregated execution architecture that decomposes transformer inference into modular units -- attention, feedforward, and MoE -- and supports disaggregated Prefill-Decode and MoE-Attention deployments. To enable disaggregation, we develop XCCL, a memory-semantic communication layer providing microsecond-level point-to-point and scalable all-to-all primitives, and we extend FlowServe with decentralized DP groups and techniques to mitigate stragglers and synchronization variance. In a peak decoding configuration, xDeepServe reaches 2400 tokens/s per Ascend 910C chip at ~50ms time-per-output-token (TPOT).

cs.DC↗

arXiv2026

U-Mind: A Unified Framework for Real-Time Multimodal Interaction with Audiovisual Generation

Full-stack multimodal interaction in real-time is a central goal in building intelligent embodied agents capable of natural, dynamic communication. However, existing systems are either limited to unimodal generation or suffer from degraded reasoning and poor cross-modal alignment, preventing coherent and perceptually grounded interactions. In this work, we introduce U-Mind, the first unified system for high-intelligence multimodal dialogue that supports real-time generation and jointly models language, speech, motion, and video synthesis within a single interactive loop. At its core, U-Mind implements a Unified Alignment and Reasoning Framework that addresses two key challenges: enhancing cross-modal synchronization via a segment-wise alignment strategy, and preserving reasoning abilities through Rehearsal-Driven Learning. During inference, U-Mind adopts a text-first decoding pipeline that performs internal chain-of-thought planning followed by temporally synchronized generation across modalities. To close the loop, we implement a real-time video rendering framework conditioned on pose and speech, enabling expressive and synchronized visual feedback. Extensive experiments demonstrate that U-Mind achieves state-of-the-art performance on a range of multimodal interaction tasks, including question answering, instruction following, and motion generation, paving the way toward intelligent, immersive conversational agents.

cs.CV↗

arXiv2026

The Topology of Recovery: Using Persistent Homology to Map Individual Mental Health Journeys in Online Communities

Understanding how individuals navigate mental health challenges over time is critical yet methodologically challenging. Traditional approaches analyze community-level snapshots, failing to capture dynamic individual recovery trajectories. We introduce a novel framework applying Topological Data Analysis (TDA) specifically persistent homology to model users' longitudinal posting histories as trajectories in semantic embedding space. Our approach reveals topological signatures of trajectory patterns: loops indicate cycling back to similar states (stagnation), while flares suggest exploring new coping strategies (growth). We propose Semantic Recovery Velocity (SRV), a novel metric quantifying the rate users move away from initial distress-focused posts in embedding space. Analyzing 15,847 r/depression trajectories and validating against multiple proxies, we demonstrate topological features predict self-reported improvement with 78.3% accuracy, outperforming sentiment baselines. This work contributes: (1) a TDA methodology for HCI mental health research, (2) interpretable topological signatures, and (3) design implications for adaptive mental health platforms with ethical guardrails.

cs.HC↗

arXiv2026

TherapyProbe: Generating Design Knowledge for Relational Safety in Mental Health Chatbots Through Adversarial Simulation

As mental health chatbots proliferate to address the global treatment gap, a critical question emerges: How do we design for relational safety the quality of interaction patterns that unfold across conversations rather than the correctness of individual responses? Current safety evaluations assess single-turn crisis responses, missing the therapeutic dynamics that determine whether chatbots help or harm over time. We introduce TherapyProbe, a design probe methodology that generates actionable design knowledge by systematically exploring chatbot conversation trajectories through adversarial multi-agent simulation. Using open-source models, TherapyProbe surfaces relational safety failures interaction patterns like "validation spirals" where chatbots progressively reinforce hopelessness, or "empathy fatigue" where responses become mechanical over turns. Our contribution is translating these failures into a Safety Pattern Library of 23 failure archetypes with corresponding design recommendations. We contribute: (1) a replicable methodology requiring no API costs, (2) a clinically-grounded failure taxonomy, and (3) design implications for developers, clinicians, and policymakers.

cs.HC↗

arXiv2026

When AI Writes, Whose Voice Remains? Quantifying Cultural Marker Erasure Across World English Varieties in Large Language Models

Large Language Models (LLMs) are increasingly used to ``professionalize'' workplace communication, often at the cost of linguistic identity. We introduce "Cultural Ghosting", the systematic erasure of linguistic markers unique to non-native English varieties during text processing. Through analysis of 22,350 LLM outputs generated from 1,490 culturally marked texts (Indian, Singaporean,& Nigerian English) processed by five models under three prompt conditions, we quantify this phenomenon using two novel metrics: Identity Erasure Rate (IER) & Semantic Preservation Score (SPS). Across all prompts, we find an overall IER of 10.26%, with model-level variation from 3.5% to 20.5% (5.9x range). Crucially, we identify a Semantic Preservation Paradox: models maintain high semantic similarity (mean SPS = 0.748) while systematically erasing cultural markers. Pragmatic markers (politeness conventions) are 1.9x more vulnerable than lexical markers (71.5% vs. 37.1% erasure). Our experiments demonstrate that explicit cultural-preservation prompts reduce erasure by 29% without sacrificing semantic quality.

cs.HC↗

arXiv2026

A Survey of Data Agents: Emerging Paradigm or Overstated Hype?

The rapid advancement of large language models (LLMs) has spurred the emergence of data agents, autonomous systems designed to orchestrate Data + AI ecosystems for tackling complex data-related tasks. However, the term "data agent" currently suffers from terminological ambiguity and inconsistent adoption, conflating simple query responders with sophisticated autonomous architectures. This terminological ambiguity fosters mismatched user expectations, accountability challenges, and barriers to industry growth. Inspired by the SAE J3016 standard for driving automation, this survey introduces the first systematic hierarchical taxonomy for data agents, comprising six levels that delineate and trace progressive shifts in autonomy, from manual operations (L0) to a vision of generative, fully autonomous data agents (L5), thereby clarifying capability boundaries and responsibility allocation. Through this lens, we offer a structured review of existing research arranged by increasing autonomy, encompassing specialized data agents for data management, preparation, and analysis, alongside emerging efforts toward versatile, comprehensive systems with enhanced autonomy. We further analyze critical evolutionary leaps and technical gaps for advancing data agents, especially the ongoing L2-to-L3 transition, where data agents evolve from procedural execution to autonomous orchestration. Finally, we conclude with a forward-looking roadmap, envisioning proactive, generative data agents.

cs.DB↗