arXiv subjects

Hailin Liu

Publications and source records attributed to Hailin Liu.

5 recordsLinked to original sources

arXiv2026

Charge Filling-Dependent Valley Polarization and its Dynamics Driven by Correlated Interlayer Exciton States in WSe2/WS2 Heterobilayer

Moiré heterostructures of transition metal dichalcogenides provide an emerging platform for studying the strongly correlated electronic and excitonic states. The valley degree of freedom and its dynamics in the strongly correlated electronic and excitonic states in the moiré system are crucial for fundamental understanding and applications of valleytronics. Although the strongly correlated states in moiré systems have been extensively investigated, their impact on valley degrees of freedom and its dynamics remains unexplored. Here, we study the charge filling-dependent valley relaxation dynamics of interlayer excitons (IXs) in WSe2/WS2 moiré heterobilayer system. The evolution of moiré IXs dynamics across the Mott transition is elucidated by combining time-resolved optical spectroscopy with Monte Carlo simulation. Our results show that the radiative decay and recombination rate of IXs are enhanced at the integer filling factors n (=-1). Moreover, the valley relaxation rate of IXs at n=$\pm$1 is significantly decreased. The resulting reduction in wavefunction overlap and phonon scattering process suppresses the nonradiative recombination and intervalley scattering of IXs. These anomalous behaviors are attributed to the modification of IXs recombination pathways and enhance excitonic coherence due to the localization of introduced charge carriers at integer filling of moiré site. Our results explore exciton and valley dynamics as sensitive probes of strongly correlated states in moiré systems and highlight possible ways to control excitonic coherence and valleytronic properties in two-dimensional quantum materials.

cond-mat.mes-hall↗

arXiv2026

Knowledge Distillation across Item and Code Spaces for Parallel Generative Recommendation

Generative recommendation with semantic IDs represents each item as a sequence of discrete codes derived from its semantic representation and predicts these codes to generate recommendations. Existing semantic-ID recommenders are typically trained with hard labels for the observed next item. Although such supervision identifies the target, it provides no explicit distributional information about alternative items conditioned on the same user history. Sequential recommenders can supply this item-level predictive knowledge, but transferring it to semantic-ID generation is challenging because the teacher and student operate in item and code spaces, respectively. We propose two complementary knowledge-distillation objectives for parallel semantic-ID recommenders, which predict all code positions simultaneously without autoregressive prefix conditioning. Item Distribution Matching (IDM) reconstructs a catalogue-level item distribution from the student's parallel code predictions and aligns it with the teacher's item distribution. Code Marginal Matching (CMM) projects the teacher's item probabilities onto each code position using fixed item-to-code assignments, then matches the resulting code marginals to the corresponding student heads. Both objectives transfer the teacher's soft distributional knowledge while preserving the student's parallel prediction structure. Experiments on benchmark datasets show that both methods consistently outperform existing state-of-the-art approaches. On average, IDM improves Recall@10 by 15.39%, while CMM improves NDCG@10 by 8.28%. These results demonstrate that distillation in either reconstructed item space or code-marginal space can substantially strengthen hard-label supervision for parallel generative recommendation.

cs.IR↗

arXiv2026

SafeAgent: A Runtime Protection Architecture for Agentic Systems

Large language model (LLM) agents are vulnerable to prompt-injection attacks that propagate through multi-step workflows, tool interactions, and persistent context, making input-output filtering alone insufficient for reliable protection. This paper presents SafeAgent, a runtime security architecture that treats agent safety as a stateful decision problem over evolving interaction trajectories. The proposed design separates execution governance from semantic risk reasoning through two coordinated components: a runtime controller that mediates actions around the agent loop and a context-aware decision core that operates over persistent session state. The core is formalized as a context-aware advanced machine intelligence and instantiated through operators for risk encoding, utility-cost evaluation, consequence modeling, policy arbitration, and state synchronization. Experiments on Agent Security Bench (ASB) and InjecAgent show that SafeAgent consistently improves robustness over baseline and text-level guardrail methods while maintaining competitive benign-task performance. Ablation studies further show that recovery confidence and policy weighting determine distinct safety-utility operating points.

cs.AI↗

arXiv2023

Image encryption for Offshore wind power based on 2D-LCLM and Zhou Yi Eight Trigrams

Offshore wind power is an important part of the new power system, due to the complex and changing situation at ocean, its normal operation and maintenance cannot be done without information such as images, therefore, it is especially important to transmit the correct image in the process of information transmission. In this paper, we propose a new encryption algorithm for offshore wind power based on two-dimensional lagged complex logistic mapping (2D-LCLM) and Zhou Yi Eight Trigrams. Firstly, the initial value of the 2D-LCLM is constructed by the Sha-256 to associate the 2D-LCLM with the plaintext. Secondly, a new encryption rule is proposed from the Zhou Yi Eight Trigrams to obfuscate the pixel values and generate the round key. Then, 2D-LCLM is combined with the Zigzag to form an S-box. Finally, the simulation experiment of the algorithm is accomplished. The experimental results demonstrate that the algorithm can resistant common attacks and has prefect encryption performance.

cs.CR↗

arXiv2022

Data Encryption based on 7D Complex Chaotic System with Cubic Memristor for Smart Grid

The information security has an irreplaceable position in the smart grid (SG). In order to avoid the malicious attack and ensure the information security, the cryptographic techniques are essential. This paper focuses on the encryption techniques to ensure the information security of SG. Firstly, an unusual 7-dimensional complex chaotic system (7D-CCS) combined with the cubic memristor is introduced. Besides its phase portraits, Lyapunov exponent, 0-1 test, complexity, and bifurcation diagram are investigated. Then, with the proposed 7D-CCS, we design a data encryption algorithm to ensure the encryption security. Finally, the data and monitoring images in SG are encrypted by the designed encryption scheme. Besides, the encryption performance is given in detailed. The experimental results show that the proposed encryption scheme has quite good encryption performance. Therefore, it can ensure the information security of SG.

cs.CR↗