arXiv subjects

Long Zhang

Publications and source records attributed to Long Zhang.

At least 91 records · Page 5Linked to original sources

arXiv2025

Topology-ferrimagnetism intertwining via weak interactions in Lieb lattices

A common wisdom about quantum many-body systems is that emergent phases typically fall into either the Landau-Ginzburg paradigm or topological classifications. Experimentally realizing the intertwined emergence of spontaneous symmetry breaking and topological order remains challenging. Here, we present an experimentally accessible platform for studying magnetic topological states in a spin-orbit-coupled Lieb lattice. Remarkably, we observe the coexistence of topological characteristics, quantified by the Chern number and Bott index, with spontaneous symmetry-breaking orders, such as ferrimagnetism, in the many-body ground states. Computational analyses combining dynamical mean-field theory and Hartree-Fock approximations reveal a pronounced parameter regime where magnetic topological insulators emerge even under weak interactions. This unconventional phenomenon originates from the Lieb lattice's unique band structure, which facilitates the synergy between interaction-driven symmetry breaking and spin-orbit coupling induced band inversion. Crucially, spin polarization and spin winding co-emerge as inherently coupled phenomena due to their shared origin in the same interacting, spinful atoms. We further propose a specific experimental implementation scheme for ultracold atoms, utilizing currently available Raman lattice techniques. Our findings pave the way for exploring the interplay between symmetry-broken states and topological order in strongly correlated systems.

cond-mat.quant-gas↗

arXiv2025

Spectroscopic Feature of Quantum Many-Body Scar States

We study the dynamical correlations of nonintegrable systems with quantum many-body scar (QMBS) states generated by a ladder operator. The spectral function of the ladder operator has an exact $δ$-function peak induced by the QMBS states. As a concrete example, we show that in the one-dimensional (1D) spin-$1$ Affleck-Kennedy-Lieb-Tasaki model, the spectral function of two-magnon excitations exhibits a characteristic bow-tie shape composed of a $δ$-function resonance peak at momentum $k=π$ and a continuum spectrum elsewhere. The two-magnon excitations can be observed with the resonant inelastic X-ray scattering spectroscopy on quasi-1D nickelates and other spin-$1$ antiferromagnetic materials, thus it paves the way to detecting the (approximate) QMBS states in realistic materials.

cond-mat.str-el↗

arXiv2025

Boundary phase transitions of two-dimensional quantum critical XXZ model

The boundary critical behavior of the two-dimensional (2D) quantum antiferromagnetic (AF) XXZ model coupled with either a dangling spin-1/2 XXZ chain or a dangling two-leg ladder on the boundary is studied with the bosonization and renormalization group analysis. A rich boundary phase diagram is obtained in each case. In the dangling chain case, the boundary either develops a long-range AF order in the easy-plane or the easy-axis direction with extraordinary critical behavior, or has a valence bond solid order with ordinary boundary critical behavior. In the case of a dangling two-leg ladder, besides the possible easy-plane or easy-axis AF ordered phases on the boundary with extraordinary critical behavior, the boundary can form a singlet phase with ordinary critical behavior without breaking any symmetry. These results are consistent with recent numerical simulations on the boundary critical behavior of 2D quantum spin models.

cond-mat.str-el↗

arXiv2025

Preparation and observation of anomalous counterpropagating edge states in a periodically driven optical Raman lattice

Motivated by the recent observation of real-space edge modes with ultracold atoms [Braun et al., Nat. Phys. 20, 1306 (2024)], we investigate the preparation and detection of anomalous counterpropagating edge states -- a defining feature of the anomalous Floquet valley-Hall (AFVH) phase -- in a two-dimensional periodically driven optical Raman lattice. Modeling the atomic cloud with a Gaussian wave packet state, we explore, both analytically and numerically, how the population of edge modes depends on the initial-state parameters. In particular, we reveal that, in addition to the internal spin state, the initial momenta parallel and perpendicular to the boundary play essential roles: they independently control the selective population of edge states across distinct momenta and within separate quasienergy gaps. Furthermore, we examine the wave-packet dynamics of counterpropagating edge states and demonstrate that their characteristic motion is robust against long-range disorder. These results establish a theoretical framework for future experimental explorations of the AFVH phase and topological phenomena associated with its unique edge modes.

cond-mat.quant-gas↗

arXiv2025

Hybrid RIS-Enhanced ISAC Secure Systems: Joint Optimization in the Presence of an Extended Target

Unlike the conventional fully-passive and fully-active reconfigurable intelligent surfaces (RISs), a hybrid RIS consisting of active and passive reflection units has recently been concerned, which can exploit their integrated advantages to alleviate the RIS-induced path loss. In this paper, we investigate a novel security strategy where the multiple hybrid RIS-aided integrated sensing and communication (ISAC) system communicates with downlink users and senses an extended target synchronously. Assuming imperfectly known target location (TL), we consider the joint design of the transmit signal and receive filter bank of the base station (BS), the receive beamformers of all users and the weights of the hybrid RIS. An optimization problem is formulated for maximizing the worst-case sensing signal-to-interference-plus-noise-ratio (SINR) subject to secure communication and system power budget constraints. To address this non-convex problem, we leverage generalized fractional programming (GFP) and penalty-dual-decomposition (PDD), and propose a security solution that efficiently optimizes all variables by employing convex optimization approaches. Simulation results show that by incorporating the multiple hybrid RIS into the optimization design, the extended target detection and secure transmission performance of ISAC systems are improved over the state-of-the-art RIS-aided ISAC approaches.

physics.ins-det↗

arXiv2025

A Framework for Spontaneous Brillouin Noise: Unveiling Fundamental Limits in Brillouin Metrology

Spontaneous Brillouin scattering (SpBS) provides a non-contact tool for probing the mechanical and thermodynamic properties of materials, enabling important applications such as distributed optical fiber sensing and high-resolution Brillouin microscopy. Achieving metrological precision in these systems relies critically on identifying fundamental noise sources. While a pioneering study three decades ago numerically investigated an intrinsic SpBS noise mechanism, this phenomenon has remained largely unexplored, particularly in the context of Brillouin metrological systems. Here, by revisiting its physical formation process and rethinking its stochastic behaviors, we develop and experimentally validate a comprehensive analytical framework on this long-overlooked noise source. Importantly, we theoretically predict, for the first time, the SpBS noise is a universal and fundamental limit that can dominate over conventional limits such as shot noise in Brillouin metrological systems like imaging, microscopy and sensing. Specifically, we experimentally demonstrate the SpBS-noise-limited regime in Brillouin imaging and sensing scenarios. This framework establishes a critical foundation for understanding and optimizing the performance bounds of current and future Brillouin-based technologies across diverse applications.

physics.optics↗

arXiv2025

Towards Efficient Partially Relevant Video Retrieval with Active Moment Discovering

Partially relevant video retrieval (PRVR) is a practical yet challenging task in text-to-video retrieval, where videos are untrimmed and contain much background content. The pursuit here is of both effective and efficient solutions to capture the partial correspondence between text queries and untrimmed videos. Existing PRVR methods, which typically focus on modeling multi-scale clip representations, however, suffer from content independence and information redundancy, impairing retrieval performance. To overcome these limitations, we propose a simple yet effective approach with active moment discovering (AMDNet). We are committed to discovering video moments that are semantically consistent with their queries. By using learnable span anchors to capture distinct moments and applying masked multi-moment attention to emphasize salient moments while suppressing redundant backgrounds, we achieve more compact and informative video representations. To further enhance moment modeling, we introduce a moment diversity loss to encourage different moments of distinct regions and a moment relevance loss to promote semantically query-relevant moments, which cooperate with a partially relevant retrieval loss for end-to-end optimization. Extensive experiments on two large-scale video datasets (\ie, TVR and ActivityNet Captions) demonstrate the superiority and efficiency of our AMDNet. In particular, AMDNet is about 15.5 times smaller (\#parameters) while 6.0 points higher (SumR) than the up-to-date method GMMFormer on TVR.

cs.CV↗

arXiv2025

Magnetic phase diagram of Cr2Te3 revisited by ac magnetostrictive coefficient

Two-dimensional (2D) magnetic materials have attracted considerable interest owing to their potential applications in spintronics and fundamental investigations into low-dimensional magnetism. Cr2Te3, a quasi 2D non van der Waals magnet, exhibits a complex magnetic phase diagram due to competing magnetic interactions within and between layers. However, the precise nature and evolution of these magnetic phases remain unclear. Here, we utilize an ultrahigh-sensitive composite magnetoelectric technique, which probes the ac magnetostrictive coefficient, to systematically explore the temperature magnetic field phase diagram of Cr2Te3 single crystals. Our results reveal the coexistence of multiple magnetic phases, including canted ferromagnetic, antiferromagnetic, and paramagnetic states. Another canted ferromagnetic phase and a possible triple point have been proposed. The updated phase diagram provides deeper insights into the specific spin configurations associated with each phase. These findings also highlight the decoupled magnetic ordering between the Cr1/Cr3 layers and the Cr2 layer near the magnetic ordering temperature.

cond-mat.mtrl-sci↗

arXiv2025

Spin pumping effect in non-Fermi liquid metals

Spin pumping effect is a sensitive and well-established experimental method in two-dimensional (2D) magnetic materials. We propose that spin pumping effect can be a valuable probe for non-Fermi liquid (NFL) behaviors at the 2D interface of magnetic heterostructures. We show that the modulations of ferromagnetic resonance exhibit power-law scalings in frequency and temperature for NFL metals induced near a quantum critical point (QCP). At the Ising nematic QCP, we demonstrate that the enhanced Gilbert damping coefficient $δα$ acquires negative power-law exponents in distinct frequency regimes. The exponents convey universal parameters inherited from the QCP and reflect the non-quasiparticle nature of the spin carriers in the NFL metal. At finite temperature, we show that the Gilbert damping mechanism is restored in the quantum critical regime and $δα$ measures the temperature dependence of the correlation length. Our theoretical proposal has the potential to stimulate the development of an interdisciplinary research domain where insights from non-equilibrium spin physics in spintronics are integrated into strongly correlated matter.

cond-mat.str-el↗

arXiv2025

Analysis of Information Loss on Composition Measurement in Stiff Chemically Reacting Systems

Gas sampling methods have been crucial for the advancement of combustion science, enabling analysis of reaction kinetics and pollutant formation. However, the measured composition can deviate from the true one because of the potential residual reactions in the sampling probes. This study formulates the initial composition estimation in stiff chemically reacting systems as a Bayesian inference problem, solved using the No-U-Turn Sampler (NUTS). Information loss arises from the restriction of system dynamics by low dimensional attracting manifold, where constrained evolution causes initial perturbations to decay or vanish in fast eigen-directions in composition space. This study systematically investigates the initial value inference in combustion systems and successfully validates the methodological framework in the Robertson toy system and hydrogen autoignition. Furthermore, a gas sample collected from a one-dimensional hydrogen diffusion flame is analyzed to investigate the effect of frozen temperature on information loss. The research highlights the importance of species covariance information from observations in improving estimation accuracy and identifies how the rank reduction in the sensitivity matrix leads to inference failures. Critical failure times for species inference in the Robertson and hydrogen autoignition systems are analyzed, providing insights into the limits of inference reliability and its physical significance.

stat.AP↗

arXiv2025

OptMetaOpenFOAM: Large Language Model Driven Chain of Thought for Sensitivity Analysis and Parameter Optimization based on CFD

Merging natural language interfaces with computational fluid dynamics (CFD) workflows presents transformative opportunities for both industry and research. In this study, we introduce OptMetaOpenFOAM - a novel framework that bridges MetaOpenFOAM with external analysis and optimization tool libraries through a large language model (LLM)-driven chain-of-thought (COT) methodology. By automating complex CFD tasks via natural language inputs, the framework empowers non-expert users to perform sensitivity analyses and parameter optimizations with markedly improved efficiency. The test dataset comprises 11 distinct CFD analysis or optimization tasks, including a baseline simulation task derived from an OpenFOAM tutorial covering fluid dynamics, combustion, and heat transfer. Results confirm that OptMetaOpenFOAM can accurately interpret user requirements expressed in natural language and effectively invoke external tool libraries alongside MetaOpenFOAM to complete the tasks. Furthermore, validation on a non-OpenFOAM tutorial case - namely, a hydrogen combustion chamber - demonstrates that a mere 200-character natural language input can trigger a sequence of simulation, postprocessing, analysis, and optimization tasks spanning over 2,000 lines of code. These findings underscore the transformative potential of LLM-driven COT methodologies in linking external tool for advanced analysis and optimization, positioning OptMetaOpenFOAM as an effective tool that streamlines CFD simulations and enhances their convenience and efficiency for both industrial and research applications. Code is available at https://github.com/Terry-cyx/MetaOpenFOAM.

cs.AI↗

arXiv2025

Measuring Non-Hermitian Topological Invariants Directly from Quench Dynamics

While non-Hermitian (NH) topological phases and phenomena have been observed across various quantum systems, directly measuring NH topological invariants remains a significant challenge. In this study, we present a generic and unified framework for the direct measurement of various NH topological invariants in odd-dimensional systems through quench dynamics. We demonstrate that in one-dimensional (1D) NH systems with sublattice symmetry, the line-gap winding number and point-gap braiding degree can be extracted from the winding patterns of a dynamically constructed field based on post-quench spin textures. Specifically, line-gap topology is characterized by integer-valued winding, whereas point-gap complex-band braiding is revealed by half-integer or integer winding with abrupt jumps. We also extend our approach to higher-dimensional winding numbers and non-Bloch topological invariants under open-boundary conditions. Additionally, we propose a practical cold-atom setup to realize and detect 1D NH topological phases, showing that our dynamical measurement scheme is feasible in current experimental settings. This work paves the way for the direct measurement of NH topological invariants in quantum systems.

cond-mat.quant-gas↗

arXiv2025

SessionRec: Next Session Prediction Paradigm For Generative Sequential Recommendation

We introduce SessionRec, a novel next-session prediction paradigm (NSPP) for generative sequential recommendation, addressing the fundamental misalignment between conventional next-item prediction paradigm (NIPP) and real-world recommendation scenarios. Unlike NIPP's item-level autoregressive generation that contradicts actual session-based user interactions, our framework introduces a session-aware representation learning through hierarchical sequence aggregation (intra/inter-session), reducing attention computation complexity while enabling implicit modeling of massive negative interactions, and a session-based prediction objective that better captures users' diverse interests through multi-item recommendation in next sessions. Moreover, we found that incorporating a rank loss for items within the session under the next session prediction paradigm can significantly improve the ranking effectiveness of generative sequence recommendation models. We also verified that SessionRec exhibits clear power-law scaling laws similar to those observed in LLMs. Extensive experiments conducted on public datasets and online A/B test in Meituan App demonstrate the effectiveness of SessionRec. The proposed paradigm establishes new foundations for developing industrial-scale generative recommendation systems through its model-agnostic architecture and computational efficiency.

cs.IR↗

arXiv2025

Simulation as Reality? The Effectiveness of LLM-Generated Data in Open-ended Question Assessment

The advancement of Artificial Intelligence (AI) has created opportunities for e-learning, particularly in automated assessment systems that reduce educators' workload and provide timely feedback to students. However, developing effective AI-based assessment tools remains challenging due to the substantial resources required for collecting and annotating real student data. This study investigates the potential and gap of simulative data to address this limitation. Through a two-phase experimental study, we examined the effectiveness and gap of Large Language Model generated synthetic data in training educational assessment systems. Our findings reveal that while simulative data demonstrates promising results in training automated assessment models, outperforming state-of-the-art GPT-4o in most question types, its effectiveness has notable limitations. Specifically, models trained on synthetic data show excellent performance in simulated environment but need progress when applied to real-world scenarios. This performance gap highlights the limitations of only using synthetic data in controlled experimental settings for AI training. The absence of real-world noise and biases, which are also present in over-processed real-world data, contributes to this limitation. We recommend that future development of automated assessment agents and other AI tools should incorporate a mixture of synthetic and real-world data, or introduce more realistic noise and biases patterns, rather than relying solely on synthetic or over-processed data.

cs.CY↗

arXiv2025

Psychometric-Based Evaluation for Theorem Proving with Large Language Models

Large language models (LLMs) for formal theorem proving have become a prominent research focus. At present, the proving ability of these LLMs is mainly evaluated through proof pass rates on datasets such as miniF2F. However, this evaluation method overlooks the varying importance of theorems. As a result, it fails to highlight the real performance disparities between LLMs and leads to high evaluation costs. This study proposes a psychometric-based evaluation method for theorem proving with LLMs, comprising two main components: Dataset Annotation and Adaptive Evaluation. First, we propose a metric calculation method to annotate the dataset with difficulty and discrimination metrics. Specifically, we annotate each theorem in the miniF2F dataset and grade them into varying difficulty levels according to the performance of LLMs, resulting in an enhanced dataset: miniF2F-Graded. Experimental results show that the difficulty grading in miniF2F-Graded better reflects the theorem difficulty perceived by LLMs. Secondly, we design an adaptive evaluation method to dynamically select the most suitable theorems for testing based on the annotated metrics and the real-time performance of LLMs. We apply this method to evaluate 10 LLMs. The results show that our method finely highlights the performance disparities between LLMs. It also reduces evaluation costs by using only 23% of the theorems in the dataset.

cs.AI↗

arXiv2025

Scaling and data collapse of two-dimensional random singlet state in magnetic field

Quenched randomness strongly affects properties of magnetic materials. Two-dimensional (2D) random singlet (RS) states emerge in random $J$-$Q$ model by destroying valence bond solid order with spatial randomness. We examine the 2D RS state in magnetic field with quantum Monte Carlo simulations. The magnetization and susceptibilities show power-law scaling with magnetic field at low temperature. Moreover, they show one-parameter scaling behavior with $B/T$, and the scaling functions are remarkably consistent with a phenomenological model of random spin pairs with a singular distribution of interactions. These universal scaling functions can be used to diagnose 2D RS states in experiments.

cond-mat.str-el↗

arXiv2025

Exciton Condensation in Landau Levels of Quantum Spin Hall Insulators

We theoretically study the quantum spin Hall insulator (QSHI) in a perpendicular magnetic field. In the noninteracting case, the QSHI with space inversion and/or uniaxial spin rotation symmetry undergoes a topological transition into a normal insulator phase at a critical magnetic field $B_{\rm c}$. The exciton condensation in the lowest Landau levels is triggered by Coulomb interactions in the vicinity of $B_{\rm c}$ at low temperature and spontaneously breaks the inversion and the spin rotation symmetries. We propose that the electron spin resonance spectroscopy with the ac magnetic field also aligned in the perpendicular direction can directly probe the exciton condensation order. Our results should apply to QSHIs such as the InAs/GaSb quantum wells and monolayer transition metal dichalcogenides.

cond-mat.mes-hall↗

arXiv2024

Dynamical scaling behavior of the two-dimensional random singlet state in the random $Q$ model

In this work, we study the scaling relation of energy and length scales in the 2D random-singlet (RS) state of the random $Q$ model. To investigate the intrinsic energy scale of the spinon subsystem arising from the model, we develop a constrained subspace update algorithm within the framework of the stochastic series expansion method (SSE) to extract the singlet-triplet gap of the system. The 2D RS state exhibits scaling behavior similar to the infinite randomness fixed point (IRFP), at least within the length scales that we simulate. Furthermore, by rescaling the system size according to the strength of randomness, we observe that the data for the excitation gap and the width of the gap distribution collapse onto a single curve. This implies that the model with different strengths of randomness may correspond to the same fixed point.

cond-mat.str-el↗