arXiv subjects

Yu Zhang

Publications and source records attributed to Yu Zhang.

At least 379 records · Page 21Linked to original sources

arXiv2026

Loschmidt echo zeros in finite-size quantum systems with linear quench

Dynamical quantum phase transitions reveal singularities in quench dynamics, characterized by the emergence of Loschmidt echo zeros at critical times, which usually exist only in the thermodynamic limit but are absent in finite-size quantum systems. In this Letter, we propose a theoretical scheme to probe Loschmidt echo zeros in finite-size systems by applying a two-step quenching protocol, which offers an experimentally feasible approach to study Loschmidt echo zeros. Using the transverse Ising model as a test bed, we identify that the exact Loschmidt echo zeros can be always accessed by tuning the quench rate, when the quench is across the phase transition point. The associated rate function displays divergence at critical times, accompanying with the change of the dynamical topological order parameter. The critical times are influenced by the quench rate, system size, and momentum modes, embodying the interplay between finite-size effects and critical dynamics. Moreover, the generality of these observations is further confirmed in the XY and Haldane models.

quant-ph↗

arXiv2026

Demonstration of Discrete-Time Quantum Walks and Observation of Topological Edge States in a Superconducting Qutrit Chain

Quantum walk serves as a versatile tool for universal quantum computing and algorithmic research. However, the implementation of discrete-time quantum walks (DTQWs) with superconducting circuits is still constrained by some limitations such as operation precision, circuit depth and connectivity. With improved hardware efficiency by using superconducting qutrits (three-level systems), we experimentally demonstrate a scalable DTQW in a superconducting circuit, observing the ballistic spreading of quantum walk in a qutrit chain. The usage of qutrits in our implementation allows hardware efficiently encoding of the walker position and the coin degree of freedom. By exploiting the flexibility and intrinsic symmetries of qutrit-based DTQWs, we successfully prepare two topological phases in the chain. For the first time, particle-hole-symmetry-protected edge states, bounded at the interface between these two topological phases, are observed in the superconducting platform. Measured parameter dependencies further validate the properties of edge states. The scalability and gate-control compatibility of the demonstrated DTQWs enable a versatile tool for superconducting quantum computing and quantum simulation.

quant-ph↗

arXiv2026

Spatio-Temporal Graph Deep Learning with Stochastic Differential Equations for Uncovering Alzheimer's Disease Progression

Identifying objective neuroimaging biomarkers to forecast Alzheimer's disease (AD) progression is crucial for timely intervention. However, this task remains challenging due to the complex dysfunctions in the spatio-temporal characteristics of underlying brain networks, which are often overlooked by existing methods. To address these limitations, we develop an interpretable spatio-temporal graph neural network framework to predict future AD progression, leveraging dual Stochastic Differential Equations (SDEs) to model the irregularly-sampled longitudinal functional magnetic resonance imaging (fMRI) data. We validate our approach on two independent cohorts, including the Open Access Series of Imaging Studies (OASIS-3) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our framework effectively learns sparse regional and connective importance probabilities, enabling the identification of key brain circuit abnormalities associated with disease progression. Notably, we detect the parahippocampal cortex, prefrontal cortex, and parietal lobule as salient regions, with significant disruptions in the ventral attention, dorsal attention, and default mode networks. These abnormalities correlate strongly with longitudinal AD-related clinical symptoms. Moreover, our interpretability strategy reveals both established and novel neural systems-level and sex-specific biomarkers, offering new insights into the neurobiological mechanisms underlying AD progression. Our findings highlight the potential of spatio-temporal graph-based learning for early, individualized prediction of AD progression, even in the context of irregularly-sampled longitudinal imaging data.

cs.LG↗

arXiv2026

Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models

Digital twins, as precise digital representations of physical systems, have evolved from passive simulation tools into intelligent and autonomous entities through the integration of artificial intelligence technologies. This paper presents a unified four-stage framework that systematically characterizes AI integration across the digital twin lifecycle, spanning modeling, mirroring, intervention, and autonomous management. By synthesizing existing technologies and practices, we distill a unified four-stage framework that systematically characterizes how AI methodologies are embedded across the digital twin lifecycle: (1) modeling the physical twin through physics-based and physics-informed AI approaches, (2) mirroring the physical system into a digital twin with real-time synchronization, (3) intervening in the physical twin through predictive modeling, anomaly detection, and optimization strategies, and (4) achieving autonomous management through large language models, foundation models, and intelligent agents. We analyze the synergy between physics-based modeling and data-driven learning, highlighting the shift from traditional numerical solvers to physics-informed and foundation models for physical systems. Furthermore, we examine how generative AI technologies, including large language models and generative world models, transform digital twins into proactive and self-improving cognitive systems capable of reasoning, communication, and creative scenario generation. Through a cross-domain review spanning eleven application domains, including healthcare, aerospace, smart manufacturing, robotics, and smart cities, we identify common challenges related to scalability, explainability, and trustworthiness, and outline directions for responsible AI-driven digital twin systems.

cs.AI↗

arXiv2026

SoulSeek: Exploring the Use of Social Cues in LLM-based Information Seeking

Social cues, which convey others' presence, behaviors, or identities, play a crucial role in human information seeking by helping individuals judge relevance and trustworthiness. However, existing LLM-based search systems primarily rely on semantic features, creating a misalignment with the socialized cognition underlying natural information seeking. To address this gap, we explore how the integration of social cues into LLM-based search influences users' perceptions, experiences, and behaviors. Focusing on social media platforms that are beginning to adopt LLM-based search, we integrate design workshops, the implementation of the prototype system (SoulSeek), a between-subjects study, and mixed-method analyses to examine both outcome- and process-level findings. The workshop informs the prototype's cue-integrated design. The study shows that social cues improve perceived outcomes and experiences, promote reflective information behaviors, and reveal limits of current LLM-based search. We propose design implications emphasizing better social-knowledge understanding, personalized cue settings, and controllable interactions.

cs.HC↗

arXiv2025

Antarctic TianMu Staring Observation Project II: Data reduction and preliminary results

The Antarctic TianMu Staring Observation Program is a time-domain optical sky survey project carried out in Antarctica, capable of large sky coverage, high-cadence sampling, and long-period staring. It utilizes the exceptional observing conditions in Antarctica to conduct high-cadence time-domain sky surveys. At present, we have successfully developed an 18-cm aperture Antarctic TianMu prototype, which has been deployed at Zhongshan Station in Antarctica for two consecutive years of trouble-free observations, during which more than 300,000 original images were obtained. This paper systematically outlines the commissioning data of the prototype telescope in 2023, the primary data processing pipeline, and the preliminary data products. The core pipeline encompasses four key stages: Data preprocessing, instrumental effect correction, astrometric solution, and full-field stellar photometry. Here, we release the 2023 data products, which specifically include reduced image data and a photometric catalog, for which, preliminary analyses demonstrate robust performance. Using Gaia Data Release 3 as a reference catalog, the astrometric precision, quantified by the root mean square of positional errors, is determined to be better than approximately 2 arcseconds, validating the observational capabilities of the system. For a 30-second exposure, the detection limit in the G-band is achieved at 15.00~mag, with a detection threshold of 1.5~$σ$. The photometric errors are below 0.1~mag for the majority of stars brighter than 14.00~mag. Furthermore, it improves significantly, reaching better than 0.01~mag for most stars brighter than 11.00~mag and 12.00~mag when employing the adaptive aperture photometry and point spread function photometry methods, respectively.

astro-ph.IM↗

arXiv2025

Chiral dual spin currents field-free perpendicular switching by altermagnet RuO2

Conventional spintronic mechanisms, such as spin-transfer and spin-orbit torques based on the spin current, rely on breaking time-reversal symmetry to manipulate magnetic moments. In contrast, for spatially separated dual spin currents, the time-reversal-invariant vector chirality emerges as a critical factor governing magnetization dynamics. Here, we investigate field-free perpendicular magnetization switching in an altermagnet RuO2/ferromagnet/heavy metal Pt trilayer, driven by chiral dual spin currents (CDSC). We demonstrate that the chirality of these dual spin currents acts as the deterministic role in breaking out-of-plane symmetry. Leveraging the intrinsic spin-splitting effect of the d-wave altermagnet to generate an x-polarized spin component, the interplay of non-collinear spin currents from two adjacent layers induces a helical magnetic texture within the intermediate layer. The resulting intralayer exchange coupling manifests as an effective in-plane magnetic field, facilitating deterministic switching. This distinct physical picture, validated by switching measurements and micromagnetic simulations, reveals that the switching polarity is dictated by chirality rather than charge current polarity. Characterized by the novel symmetry and low power consumption, CDSC offers a promising paradigm for next-generation high-performance spintronic architectures.

cond-mat.mes-hall↗

arXiv2025

On-chip quadratically nonlinear photodetector

Involving deterministically nonlinear photoresponse in on-chip photodetector is intriguing to develop sophisticated functions in photonic integrated circuits, such as in-sensor computing and optoelectronic mixing, though the corresponding devices are still lack of sufficient investigation. Here, we demonstrate an on-chip quadratically nonlinear photodetector (QNPD) by configuring an InSe p-i-n homojunction on a silicon waveguide. Telecom-band light guiding in the waveguide couples with the InSe evanescently and is frequency up-converted into visible light via InSe's second-harmonic generation (SHG), which is subsequently absorbed by InSe and finally generates photocurrent under the built-in electric field of the p-i-n homojunction. Governed by these sequential processes, the on-chip QNPD presents a quadratic function between photocurrent and optical power. Thanks to the efficient SHG and well-established homojunction in InSe, the QNPD reaches a high normalized responsivity of 37.1 A/W2 and low dark current of 1 pA, representing greatly improved performances among reported nonlinear photodetectors. Benefiting from the extra SHG process, the on-chip QNPD intrinsically incorporates light-light interactions, enabling straightforwardly monitoring all-optically mixing signals electrically. As an example, an array of 16-pixel QNPDs was designed to implement a fully single-shot on-chip autocorrelator without requirement of bulky optics and external cameras, which precisely measures picosecond pulses with high sensitivity of 6.1*10-10 W2.

physics.optics↗

arXiv2025

Information-Scrambling-Enhanced Quantum Sensing Beyond the Standard Quantum Limit

Quantum sensing promises measurement precision beyond classical limits, but its practical realization is often hindered by decoherence and the challenges of generating and stabilizing entanglement in large-scale systems. Here, we experimentally demonstrate a scalable, scrambling-enhanced quantum sensing protocol, referred to as butterfly metrology, implemented on a cross-shaped superconducting quantum processor. By harnessing quantum information scrambling, the protocol converts local interactions into delocalized metrologically useful correlations, enabling robust signal amplification through interference of the scrambled and polarized quantum states. We validate the time-reversal ability via Loschmidt echo measurements and quantify the information scrambling through out-of-time-ordered correlators, establishing the essential quantum resources of our protocol. Our measurements reveal that the sensing sensitivity surpasses the standard quantum limit (SQL) with increasing qubit number, reaching 3.78 in a 9-qubit configuration, compared to the SQL of 3.0. The scheme further exhibits inherent robustness to coherent control errors and probed signal noise. This work demonstrates a readily scalable path toward practical quantum sensing advantages with prevalent experimental platforms.

quant-ph↗

arXiv2025

From Pixels to Predicates Structuring urban perception with scene graphs

Perception research is increasingly modelled using streetscapes, yet many approaches still rely on pixel features or object co-occurrence statistics, overlooking the explicit relations that shape human perception. This study proposes a three stage pipeline that transforms street view imagery (SVI) into structured representations for predicting six perceptual indicators. In the first stage, each image is parsed using an open-set Panoptic Scene Graph model (OpenPSG) to extract object predicate object triplets. In the second stage, compact scene-level embeddings are learned through a heterogeneous graph autoencoder (GraphMAE). In the third stage, a neural network predicts perception scores from these embeddings. We evaluate the proposed approach against image-only baselines in terms of accuracy, precision, and cross-city generalization. Results indicate that (i) our approach improves perception prediction accuracy by an average of 26% over baseline models, and (ii) maintains strong generalization performance in cross-city prediction tasks. Additionally, the structured representation clarifies which relational patterns contribute to lower perception scores in urban scenes, such as graffiti on wall and car parked on sidewalk. Overall, this study demonstrates that graph-based structure provides expressive, generalizable, and interpretable signals for modelling urban perception, advancing human-centric and context-aware urban analytics.

cs.CV↗

arXiv2025

The Social Blindspot in Human-AI Collaboration: How Undetected AI Personas Reshape Team Dynamics

As generative AI systems become increasingly embedded in collaborative work, they are evolving from visible tools into human-like communicative actors that participate socially rather than merely providing information. Yet little is known about how such agents shape team dynamics when their artificial nature is not recognised, a growing concern as human-like AI is deployed at scale in education, organisations, and civic contexts where collaboration underpins collective outcomes. In a large-scale mixed-design experiment (N = 905), we examined how AI teammates with distinct communicative personas, supportive or contrarian, affected collaboration across analytical, creative, and ethical tasks. Participants worked in triads that were fully human or hybrid human-AI teams, without being informed of AI involvement. Results show that participants had limited ability to detect AI teammates, yet AI personas exerted robust social effects. Contrarian personas reduced psychological safety and discussion quality, whereas supportive personas improved discussion quality without affecting safety. These effects persisted after accounting for individual differences in detectability, revealing a dissociation between influence and awareness that we term the social blindspot. Linguistic analyses confirmed that personas were enacted through systematic differences in affective and relational language, with partial mediation for discussion quality but largely direct effects on psychological safety. Together, the findings demonstrate that AI systems can tacitly regulate collaborative norms through persona-level cues, even when users remain unaware of their presence. We argue that persona design constitutes a form of social governance in hybrid teams, with implications for the responsible deployment of AI in collective settings.

cs.HC↗

arXiv2025

SN 2024aecx: a fast-evolving Type IIb supernova with a prominent shock-cooling peak

SN 2024aecx is a nearby ($\sim$11 Mpc) Type IIb SN discovered within $\sim$1 d after explosion. In this paper we report high-cadence photometric (typically 0.5$\sim$1 day) and spectroscopic follow-up observations, conducted from as early as 0.27 d post discovery out to the nebular phase at 158.4 d. We analyze the environment of SN 2024aecx and derive a new distance (11.3$\pm$1.1 Mpc), metallicity and host extinction. The light curve exhibits a hot and luminous shock-cooling peak at the first few days, followed by a main peak with very rapid post-maximum decline. The earliest spectra are blue and featureless, while from 2.3 d after discovery prominent P-Cygni profiles emerge. At nebular phase, the emission lines exhibit asymmetric and double-peaked profiles, indicating asphericity and/or early dust formation in the ejecta. Nebular spectral modelling indicates a blueshifted O-rich clump moving toward observer, and the $[\text{OI}]/[\text{CaII}]$ line ratio suggests an intermediate-mass progenitor. We simulated the progenitor and explosion using a two-component model of shock cooling and radioactive $^{56}$Ni heating; our model favors an extended, low-mass H-rich envelope with $M_{\mathrm{e}} = 0.04\pm{0.01} M_{\odot}$ and a low ejecta mass of$M_{\mathrm{ej}} = 1.55^{+0.18}_{-0.14} M_{\odot}$. And the nebular-phase spectra and light-curve modelling both suggest that it most likely originated from an intermediate-mass binary progenitor system. The comprehensive monitoring of SN 2024aecx, coupled with the detailed characterization of its local environment, establishes it as a benchmark event for probing the progenitors and explosion mechanisms of Type IIb SNe.

astro-ph.SR↗

arXiv2025

Topological pressure and equilibrium state for certain correspondences

In \cite{Miller-Akin1999}, Miller and Akin investigated the invariant measures for correspondences, which are also known as upper semi-continuous set-valued maps. Recently, the variational principle and thermodynamic formalism for forward expansive correspondences were studied by Li, Li and Zhang \cite{Xiaoran Li-Zhiqiang Li-Yiwei Zhang2023}. In this paper, the invariant measures and the associated transition probability kernels are explicitly expressed for certain correspondences satisfying the assumptions in \cite{Xiaoran Li-Zhiqiang Li-Yiwei Zhang2023} via the equilibrium states of some particular potentials. Let $T$ be a correspondence on a closed connected Riemannian manifold generated by finite $C^{2}$-expanding endomorphisms. When the generators of $T$ have no coincidence point, a locally Hölder continuous potential $ϕ$ of two variables is defined via the Jacobians of the generators. The pressure of $ϕ$ and its equilibrium state $(μ, \mathcal{Q})$ are obtained, where $μ$ is a $T$-invariant measure which is absolutely continuous with respect to the volume and $\mathcal{Q}$ is the associated transition probability kernel satisfying $μ\mathcal{Q}=μ$. For the correspondence $T$ on the torus whose generators have coincidence points, the variational topological pressures for measurable potentials are introduced and the corresponding equilibrium states are considered. Moreover, the uniqueness of the equilibrium states of correspondences is considered via the natural extensions.

math.DS↗

arXiv2025

A Fractional Calculus Framework for Open Quantum Dynamics: From Liouville to Lindblad to Memory Kernels

Open quantum systems exhibit dynamics ranging from unitary evolution to irreversible dissipation. While the Gorini--Kossakowski--Sudarshan--Lindblad (GKSL) equation uniquely characterizes Markovian CPTP evolution, many physical platforms display non-Markovian features such as algebraic relaxation and coherence backflow. Fractional calculus provides a natural way to model such long-memory behavior through power-law temporal kernels introduced by fractional time derivatives. Here we develop a unified framework that embeds fractional master equations within the broader hierarchy of open-system formalisms. The fractional equation forms a structured subclass of memory-kernel models, reduces to the Lindblad form at unit order, and, through Bochner--Phillips subordination, admits a CPTP representation as an average over Lindblad semigroups. Its resolvent structure further connects fractional dynamics to established non-Markovian approaches, including Nakajima--Zwanzig kernels and hierarchical equations of motion, providing a compact surrogate for long-memory effects. This formulation positions fractional calculus as a rigorous and practical language for quantum dynamics with intrinsic memory, supporting both analytical insight and efficient quantum simulation.

quant-ph↗

arXiv2025

FutureX: Enhance End-to-End Autonomous Driving via Latent Chain-of-Thought World Model

In autonomous driving, end-to-end planners learn scene representations from raw sensor data and utilize them to generate a motion plan or control actions. However, exclusive reliance on the current scene for motion planning may result in suboptimal responses in highly dynamic traffic environments where ego actions further alter the future scene. To model the evolution of future scenes, we leverage the World Model to represent how the ego vehicle and its environment interact and change over time, which entails complex reasoning. The Chain of Thought (CoT) offers a promising solution by forecasting a sequence of future thoughts that subsequently guide trajectory refinement. In this paper, we propose FutureX, a CoT-driven pipeline that enhances end-to-end planners to perform complex motion planning via future scene latent reasoning and trajectory refinement. Specifically, the Auto-think Switch examines the current scene and decides whether additional reasoning is required to yield a higher-quality motion plan. Once FutureX enters the Thinking mode, the Latent World Model conducts a CoT-guided rollout to predict future scene representation, enabling the Summarizer Module to further refine the motion plan. Otherwise, FutureX operates in an Instant mode to generate motion plans in a forward pass for relatively simple scenes. Extensive experiments demonstrate that FutureX enhances existing methods by producing more rational motion plans and fewer collisions without compromising efficiency, thereby achieving substantial overall performance gains, e.g., 6.2 PDMS improvement for TransFuser on NAVSIM. Code will be released.

cs.CV↗

arXiv2025

Visualization Generation with Large Language Models: An Evaluation

The frequent need for analysts to create visualizations to derive insights from data has driven extensive research into the generation of natural Language to Visualization (NL2VIS). While recent progress in large language models (LLMs) suggests their potential to effectively support NL2VIS tasks, existing studies lack a systematic investigation into the performance of different LLMs under various prompt strategies. This paper addresses this gap and contributes a crucial baseline evaluation of LLMs' capabilities in generating visualization specifications of NL2VIS tasks. Our evaluation utilizes the nvBench dataset, employing six representative LLMs and eight distinct prompt strategies to evaluate their performance in generating six target chart types using the Vega-Lite visualization specification. We assess model performance with multiple metrics, including vis accuracy, validity and legality. Our results reveal substantial performance disparities across prompt strategies, chart types, and LLMs. Furthermore, based on the evaluation results, we uncover several counterintuitive behaviors across these dimensions, and propose directions for enhancing the NL2VIS benchmark to better support future NL2VIS research.

cs.HC↗

arXiv2025

AgriGPT-Omni: A Unified Speech-Vision-Text Framework for Multilingual Agricultural Intelligence

Despite rapid advances in multimodal large language models, agricultural applications remain constrained by the lack of multilingual speech data, unified multimodal architectures, and comprehensive evaluation benchmarks. To address these challenges, we present AgriGPT-Omni, an agricultural omni-framework that integrates speech, vision, and text in a unified framework. First, we construct a scalable data synthesis and collection pipeline that converts agricultural texts and images into training data, resulting in the largest agricultural speech dataset to date, including 492K synthetic and 1.4K real speech samples across six languages. Second, based on this, we train the first agricultural omni-model via a three-stage paradigm: textual knowledge injection, progressive multimodal alignment, and GRPO-based reinforcement learning, enabling unified reasoning across languages and modalities. Third, we propose AgriBench-Omni-2K, the first tri-modal benchmark for agriculture, covering diverse speech-vision-text tasks and multilingual slices, with standardized protocols and reproducible tools. Experiments show that AgriGPT-Omni significantly outperforms general-purpose baselines on multilingual and multimodal reasoning as well as real-world speech understanding. All models, data, benchmarks, and code will be released to promote reproducible research, inclusive agricultural intelligence, and sustainable AI development for low-resource regions.

cs.CL↗

arXiv2025

Anomalous collective modes in atomic nuclei within the proton-neutron interacting boson model

Novel collective modes characterized by a $B_{4/2}$ ratio ($\equiv B(E2;4_1^+\rightarrow 2_1^+)/B(E2;2_1^+\rightarrow 0_1^+)$) less than 1.0 that were observed recently have been identified within the proton-neutron interacting boson model (IBM-2) using the consistent-$Q$ Hamiltonian. These modes are shown to give rise to triaxial spectral features, including significant band mixing. The results provide a compelling explanation for the deeply suppressed $B_{4/2}$ ratio observed in $^{166}$W, $^{168,170}$Os, and $^{172}$Pt, offering new insights into the $B(E2)$ anomaly phenomenon in neutron-deficient nuclei.

nucl-th↗