arXiv subjects

Ming Xie

Publications and source records attributed to Ming Xie.

At least 19 recordsLinked to original sources

arXiv2026

Layer-Hybridized Wigner Crystals in MoSe2/WS2 Moir\'e Superlattice

Transition metal dichalcogenide moir\'e heterobilayers with type-II band alignment provide a versatile platform for layer-polarized generalized Wigner crystals, in which strong Coulomb interactions drive charge ordering at fractional lattice fillings. With a finite interlayer band offset, an out-of-plane electric field can tune layer-resolved moir\'e bands through resonance and enable controllable interlayer hybridization. Although hybridized Mott insulators have been previously demonstrated, whether fractional charge-ordered states can survive such hybridization remains elusive. Here we drive an H-stacked MoSe2/WS2 moir\'e heterobilayer through a type-I-to-type-II band-alignment transition and realize layer-hybridized Mott insulator and generalized Wigner crystals. For fillings below one electron per moir\'e cell, tunneling delocalizes electrons and modifies Wigner crystallization. However, above one electron per cell, Coulomb repulsion overcomes tunneling and favors layer-separated occupation, stabilizing stronger charge-ordered states. These results establish electrically tunable hybridized moir\'e heterobilayers as a powerful platform for engineering correlated charge order and exploring fractional Chern phases and emergent magnetism.

cond-mat.mes-hall↗

arXiv2026

StreamOV: Streaming Omni-Video Understanding via Evidence-Guided Memory and Response Triggering

While streaming omni-video understanding demands continuous perception and proactive, real-time interaction, this crucial area remains largely under-explored. Current omni-modal methods are inherently designed for offline settings, limiting their applicability in streaming scenarios due to two fundamental flaws. First, they lack robust mechanisms to manage continuously growing audio-visual context over long horizons and cannot autonomously initiate responses at opportune moments. Second, existing benchmarks are predominantly confined to offline, single-turn question answering, failing to capture continuous, multi-turn streaming interactions. To bridge these gaps, we propose StreamOV, a novel Streaming Omni-Video understanding framework for efficient online audio-visual reasoning with bounded memory and proactive response triggering. Specifically, StreamOV introduces a multimodal evidence-guided long-short term memory that condenses historical audio-visual context into compact informative evidence under a fixed budget. It further employs a hidden-state-driven trigger to decide when to respond, avoiding explicit silence-token generation and external routers. We also curate SOVBench, the first comprehensive benchmark for online, multi-turn omni-modal evaluation. Extensive experiments show that StreamOV achieves state-of-the-art performance across diverse streaming and omni-video benchmarks, demonstrating its effectiveness for both online and offline video understanding.

cs.CV↗

arXiv2026

Collective Spin Excitations in Correlated Moir\'e Chern Ferromagnets

Moir\'e-induced narrow electronic bands in transition metal dichalcogenide superlattices support many correlated quantum phases characterized by novel charge, flavor, and topological orders. Among these, magnetic ordering emerges as the most ubiquitous, often serving as the parent state for other correlated phases, including quantum anomalous Hall states, as well as chiral superconducting state. Because of electron-electron correlation, the stability of magnetic order is critically influenced by low-energy collective spin fluctuations, or magnon excitations. We investigate the nature of magnon excitations and their impact on the stability and transition temperature of the magnetic state at integer filling factor $\nu = -1$. We find that the magnon spectrum exhibits isolated low-energy bands whose topological character undergoes a transition upon tuning the interlayer displacement field. The magnon gap is found to depend sensitively on the topology of the magnetic ground state, resulting in an order-of-magnitude enhancement of the transition temperature $T_c$ in the quantum anomalous Hall phase compared to the topologically trivial correlated insulator. Our findings provide insight into the interplay between electron and magnon topology and suggest new routes for controlling magnetism and topology via moir\'e engineering.

cond-mat.str-el↗

arXiv2025

Are Bus-Mounted Edge Servers Feasible?

Placement of edge servers is the prerequisite of provisioning edge computing services for Internet of Vehicles (IoV). Fixed-site edge servers at Road Side Units (RSUs) or base stations are able to offer basic service coverage for end users, i.e., vehicles on road. However, the server locations and capacity are fixed after deployment, rendering their inefficiency in handling spationtemporal user dynamics. Mobile servers such as buses, on the other hand, have the potential of adding computation elasticity to such system. To this end, this paper studies the feasibility of bus-mounted edge servers based on real traces. First, we investigate the coverage of the buses and base stations using the Shanghai bus/taxi/Telecom datasets, which shows a great potential of bus-based edge servers as they cover a great portion of geographic area and demand points. Next, we build a mathematical model and design a simple greedy heuristic algorithm to select a limited number of buses that maximizes the coverage of demand points, i.e., with a limited purchase budget. We perform trace-driven simulations to verify the performance of the proposed bus selection algorithm. The results show that our approach effectively handles the dynamic user demand under realistic constraints such as server capacity and purchase quantity. Thus, we claim: bus-mounted edge servers for vehicular networks in urban areas are feasible, beneficial, and valuable.

cs.DC↗

arXiv2025

Enhancing Video Inpainting with Aligned Frame Interval Guidance

Recent image-to-video (I2V) based video inpainting methods have made significant strides by leveraging single-image priors and modeling temporal consistency across masked frames. Nevertheless, these methods suffer from severe content degradation within video chunks. Furthermore, the absence of a robust frame alignment scheme compromises intra-chunk and inter-chunk spatiotemporal stability, resulting in insufficient control over the entire video. To address these limitations, we propose VidPivot, a novel framework that decouples video inpainting into two sub-tasks: multi-frame consistent image inpainting and masked area motion propagation. Our approach introduces frame interval priors as spatiotemporal cues to guide the inpainting process. To enhance cross-frame coherence, we design a FrameProp Module that implements a frame content propagation strategy, diffusing reference frame content into subsequent frames via a splicing mechanism. Additionally, a dedicated context controller encodes these coherent frame priors into the I2V generative backbone, effectively serving as soft constrain to suppress content distortion during generation. Extensive evaluations demonstrate that VidPivot achieves competitive performance across diverse benchmarks and generalizes well to different video inpainting scenarios.

cs.CV↗

arXiv2025

Large Language Model-driven Security Assistant for Internet of Things via Chain-of-Thought

The rapid development of Internet of Things (IoT) technology has transformed people's way of life and has a profound impact on both production and daily activities. However, with the rapid advancement of IoT technology, the security of IoT devices has become an unavoidable issue in both research and applications. Although some efforts have been made to detect or mitigate IoT security vulnerabilities, they often struggle to adapt to the complexity of IoT environments, especially when dealing with dynamic security scenarios. How to automatically, efficiently, and accurately understand these vulnerabilities remains a challenge. To address this, we propose an IoT security assistant driven by Large Language Model (LLM), which enhances the LLM's understanding of IoT security vulnerabilities and related threats. The aim of the ICoT method we propose is to enable the LLM to understand security issues by breaking down the various dimensions of security vulnerabilities and generating responses tailored to the user's specific needs and expertise level. By incorporating ICoT, LLM can gradually analyze and reason through complex security scenarios, resulting in more accurate, in-depth, and personalized security recommendations and solutions. Experimental results show that, compared to methods relying solely on LLM, our proposed LLM-driven IoT security assistant significantly improves the understanding of IoT security issues through the ICoT approach and provides personalized solutions based on the user's identity, demonstrating higher accuracy and reliability.

cs.CR↗

arXiv2025

Probing Quantum Anomalous Hall States in Twisted Bilayer WSe2 via Attractive Polaron Spectroscopy

Moir\'e superlattices in semiconductors exhibit a rich variety of interaction-induced topological states, including quantum anomalous Hall (QAH) effects. A recent study hinted that twisted WSe2 homobilayer (tWSe2) could host a QAH state but lacked direct evidence of ferromagnetism, a key hallmark of this phase. Here, we report the first direct evidence of QAH states in tWSe2 with spontaneous ferromagnetism. Specifically, we employ polarization-resolved attractive polaron spectroscopy on a dual-gated, 2 degree tWSe2 and observe direct signatures of spontaneous time-reversal symmetry breaking at hole filling \nu = 1. Together with a Chern number measurement via Streda formula analysis, we identify this magnetized state as a topological state, characterized by C = 1. Furthermore, we demonstrate that these topological and magnetic properties are tunable via a finite displacement field, between a QAH ferromagnetic state and an antiferromagnetic state. Our findings position tWSe2 as a highly versatile, stable, and optically addressable platform for investigating topological order and strong correlations in two-dimensional landscapes.

cond-mat.str-el↗

arXiv2025

AnyRefill: A Unified, Data-Efficient Framework for Left-Prompt-Guided Vision Tasks

In this paper, we present a novel Left-Prompt-Guided (LPG) paradigm to address a diverse range of reference-based vision tasks. Inspired by the human creative process, we reformulate these tasks using a left-right stitching formulation to construct contextual input. Building upon this foundation, we propose AnyRefill, an extension of LeftRefill, that effectively adapts Text-to-Image (T2I) models to various vision tasks. AnyRefill leverages the inpainting priors of advanced T2I model based on the Diffusion Transformer (DiT) architecture, and incorporates flexible components to enhance its capabilities. By combining task-specific LoRAs with the stitching input, AnyRefill unlocks its potential across diverse tasks, including conditional generation, visual perception, and image editing, without requiring additional visual encoders. Meanwhile, AnyRefill exhibits remarkable data efficiency, requiring minimal task-specific fine-tuning while maintaining high generative performance. Through extensive ablation studies, we demonstrate that AnyRefill outperforms other image condition injection methods and achieves competitive results compared to state-of-the-art open-source methods. Notably, AnyRefill delivers results comparable to advanced commercial tools, such as IC-Light and SeedEdit, even in challenging scenarios. Comprehensive experiments and ablation studies across versatile tasks validate the strong generation of the proposed simple yet effective LPG formulation, establishing AnyRefill as a unified, highly data-efficient solution for reference-based vision tasks.

cs.CV↗

arXiv2025

Transformability reveals the interplay of dynamics across different network orders

Recent studies have investigated various dynamic processes characterizing collective behaviors in real-world systems. However, these dynamics have been studied individually in specific contexts. In this article, we present a holistic analysis framework that bridges the interplays between dynamics across networks of different orders, demonstrating that these processes are not independent but can undergo systematic transformations. Focusing on contagion dynamics, we identify and quantify dynamical and structural factors that explains the interplay between dynamics on higher-order and pairwise networks, uncovering a universal model for system instability governed by these factors. Furthermore, we validate the findings from contagion dynamics to opinion dynamics, highlighting its broader applicability across diverse dynamical processes. Our findings reveal the intrinsic coupling between diverse dynamical processes, providing fresh insights into the distinct role of complex dynamics governed by higher-order interactions.

physics.app-ph↗

arXiv2024

Thermal crossover from a Chern insulator to a fractional Chern insulator in pentalayer graphene

By theoretically analyzing the recent temperature dependent transport data in pentalayer graphene [Lu et al., arXiv:2408.10203], we establish that the experimentally observed transition from low-temperature quantum anomalous Hall effect to higher-temperature fractional quantum anomalous Hall effect is a crossover phenomenon arising from the competition between interaction and disorder energy scales, with the likely zero temperature ground state of the system being either a localized insulator or a Chern insulator with a quantized anomalous Hall effect. In particular, the intriguing suppression of FQAHE in favor of QAHE with decreasing temperature is explained as arising from the low-temperature localization of the carriers where disorder overcomes the interaction effects. We provide a detailed analysis of the data in support of the crossover scenario.

cond-mat.mes-hall↗

arXiv2024

Superconductivity in twisted transition metal dichalcogenide homobilayers

For the first time, robust superconductivity has been independently observed in twisted WSe$_2$ bilayers by two separate groups [Y. Xia et al., arXiv:2405.14784; Y. Guo et al., arXiv:2406.03418.]. In light of this, we explore the possibility of a universal superconducting pairing mechanism in twisted WSe$_2$ bilayers. Using a continuum band structure model and a phenomenological boson-mediated effective electron-electron attraction, we find that intervalley intralayer pairing predominates over interlayer pairing. Notably, despite different experimental conditions, both twisted WSe$_2$ samples exhibit a comparable effective attraction strength. This consistency suggests that the dominant pairing glue is likely independent of the twist angle and layer polarization, pointing to a universal underlying boson-induced pairing mechanism.

cond-mat.supr-con↗

arXiv2024

Integer and fractional quantum anomalous Hall effects in pentalayer graphene

We critically analyze the recently reported observation of integer (IQAHE) and fractional (FQAHE) quantum anomalous Hall effects at zero applied magnetic field in pentalayer graphene. Our quantitative activation and variable range hopping transport analysis of the experimental data reveals that the observed IQAHE and FQAHE at different fillings all have similar excitation gaps of the order of $5-10$ K. In addition, we also find that the observed FQAHE manifests a large hidden background contact series resistance >10 k$\Omega$ of unknown origin whereas this contact resistance is much smaller ~500 $\Omega$ in the observed IQAHE. Both of these findings are surprising as well as inconsistent with the well-established phenomenology of the corresponding high-field integer and fractional quantum Hall effects in 2D semiconductor systems.

cond-mat.mes-hall↗

arXiv2024

Long-lived Topological Flatband Excitons in Semiconductor Moir\'e Heterostructures: a Bosonic Kane-Mele Model Platform

Moir\'e superlattices based on two-dimensional transition metal dichalcogenides (TMDs) have emerged as a highly versatile and fruitful platform for exploring correlated topological electronic phases. One of the most remarkable examples is the recently discovered fractional quantum anomalous Hall effect (FQAHE) under zero magnetic field. Here we propose a minimal structure that hosts long-lived excitons -- a ubiquitous bosonic excitation in TMD semiconductors -- with narrow topological bosonic bands. The nontrivial exciton topology originates from hybridization of moir\'e interlayer excitons, and is tunable by controlling twist angle and electric field. At small twist angle, the lowest exciton bands are isolated from higher energy bands and provide a solid-state realization of the bosonic Kane-Mele model with topological flatbands, which could potentially support the bosonic version of FQAHE.

cond-mat.mes-hall↗

arXiv2024

On the zero-field quantization of the anomalous quantum Hall effect in moir\'e 2D layers

In recent breakthrough experiments, twisted moir\'e layers of transition metal dichalcogenides are found to manifest both integer (IQAHE) and fractional (FQAHE) quantum anomalous Hall effects in zero applied magnetic field because of the underlying flat band topology and spontaneous breaking of the time reversal invariance. In the current work, we critically analyze the experimental values of the quantized conductance in each case to emphasize the role of disorder in the problem, pointing out that obtaining accurate quantized conductance in future experiments would necessitate better contacts and lower disorder.

cond-mat.mes-hall↗

arXiv2023

Electrical control and transport of tightly bound interlayer excitons in a MoSe2/hBN/MoSe2 heterostructure

Controlling interlayer excitons in van der Waals heterostructures holds promise for exploring Bose-Einstein condensates and developing novel optoelectronic applications, such as excitonic integrated circuits. Despite intensive studies, several key fundamental properties of interlayer excitons, such as their binding energies and interactions with charges, remain not well understood. Here we report the formation of momentum-direct interlayer excitons in a high-quality MoSe2/hBN/MoSe2 heterostructure under an electric field, characterized by bright photoluminescence (PL) emission with high quantum yield and a narrow linewidth of less than 4 meV. These interlayer excitons show electrically tunable emission energy spanning ~180 meV through the Stark effect, and exhibit a sizable binding energy of ~81 meV in the intrinsic regime, along with trion binding energies of a few millielectronvolts. Remarkably, we demonstrate the long-range transport of interlayer excitons with a characteristic diffusion length exceeding ten micrometers, which can be attributed, in part, to their dipolar repulsive interactions. Spatially and polarization-resolved spectroscopic studies reveal rich exciton physics in the system, such as valley polarization, local trapping, and the possible existence of dark interlayer excitons. The formation and transport of tightly bound interlayer excitons with narrow linewidth, coupled with the ability to electrically manipulate their properties, open exciting new avenues for exploring quantum many-body physics, including excitonic condensate and superfluidity, and for developing novel optoelectronic devices, such as exciton and photon routers.

cond-mat.mes-hall↗

arXiv2023

Influence maximization in multilayer networks based on adaptive coupling degree

Influence Maximization(IM) aims to identify highly influential nodes to maximize influence spread in a network. Previous research on the IM problem has mainly concentrated on single-layer networks, disregarding the comprehension of the coupling structure that is inherent in multilayer networks. To solve the IM problem in multilayer networks, we first propose an independent cascade model (MIC) in a multilayer network where propagation occurs simultaneously across different layers. Consequently, a heuristic algorithm, i.e., Adaptive Coupling Degree (ACD), which selects seed nodes with high spread influence and a low degree of overlap of influence, is proposed to identify seed nodes for IM in a multilayer network. By conducting experiments based on MIC, we have demonstrated that our proposed method is superior to the baselines in terms of influence spread and time cost in 6 synthetic and 4 real-world multilayer networks.

physics.soc-ph↗

arXiv2023

Nematic excitonic insulator in transition metal dichalcogenide moiré heterobilayers

We study the effect of inter-electron Coulomb interactions on the displacement field induced topological phase transition in transition metal dichalcogenide (TMD) moiré heterobilayers. We find a nematic excitonic insulator (NEI) phase that breaks the moiré superlattice's three-fold rotational symmetry and preempts the topological phase transition in both AA and AB stacked heterobilayers when the interlayer tunneling is weak, or when the Coulomb interaction is not strongly screened. The nematicity originates from the frustration between the nontrivial spatial structure of the interlayer tunneling, which is crucial to the existence of the topological Chern band, and the interlayer coherence induced by the Coulomb interaction that favors uniformity in layer pseudo-spin orientations. We construct a unified effective two-band model that captures the physics near the band inversion and applies to both AA and AB stacked heterobilayers. Within the two-band model, the competition between the NEI phase and the Chern insulator phase can be understood as the switching of the energetic order between the $s$-wave and the $p$-wave excitons upon increasing the interlayer tunneling.

cond-mat.str-el↗

arXiv2023

A universal meta-heuristic framework for influence maximization in hypergraphs

Influence maximization (IM) aims to select a small number of nodes that are able to maximize their influence in a network and covers a wide range of applications. Despite numerous attempts to provide effective solutions in ordinary networks, higher-order interactions between entities in various real-world systems are not usually taken into account. In this paper, we propose a versatile meta-heuristic approach, hyper genetic algorithm (HGA), to tackle the IM problem in hypergraphs, which is based on the concept of genetic evolution. Systematic validations in synthetic and empirical hypergraphs under both simple and complex contagion models indicate that HGA achieves universal and plausible performance compared to baseline methods. We explore the cause of the excellent performance of HGA through ablation studies and correlation analysis. The findings show that the solution of HGA is distinct from that of other prior methods. Moreover, a closer look at the local topological features of the seed nodes acquired by different algorithms reveals that the selection of seed nodes cannot be based on a single topological characteristic, but should involve a combination of multiple topological features to address the IM problem.

physics.soc-ph↗