arXiv subjects

Guoli Wu

Publications and source records attributed to Guoli Wu.

4 recordsLinked to original sources

arXiv2026

Symmetry-Enforced Ferroelectric Switching of Two-Dimensional Altermagnetism

Altermagnetism features strong momentum-dependent spin splitting despite zero net magnetization, offering a transformative platform for next-generation spintronics. However, the nonvolatile and deterministic switching between its two equivalent spin-splitting states remains a fundamental bottleneck. Here, we propose a universal layer-engineering paradigm to achieve symmetry-enforced ferroelectric switching of two-dimensional altermagnetism. By sandwiching a conventional antiferromagnetic monolayer between two identical ferroelectric layers, the out-of-plane polarization cleanly breaks the spatial symmetry to induce robust altermagnetic splitting. Crucially, the global combined parity-time symmetry dictates that reversing the ferroelectric polarization exactly inverts the altermagnetic spin-splitting pattern. We rigorously validate this mechanism in the In2Se3/MnPTe3/In2Se3 trilayer using first-principles calculations. As a direct consequence, the ferroelectrically driven spin-splitting reversal deterministically flips the anomalous Hall effect signal, providing an unambiguous transport fingerprint to electrically distinguish the two altermagnetic states. Unconstrained by the stringent symmetry requirements of intrinsic single-phase materials, our findings establish a versatile physical framework for electrically addressable altermagnetic spintronics.

cond-mat.mtrl-sci↗

arXiv2024

Contrastive Representation for Interactive Recommendation

Interactive Recommendation (IR) has gained significant attention recently for its capability to quickly capture dynamic interest and optimize both short and long term objectives. IR agents are typically implemented through Deep Reinforcement Learning (DRL), because DRL is inherently compatible with the dynamic nature of IR. However, DRL is currently not perfect for IR. Due to the large action space and sample inefficiency problem, training DRL recommender agents is challenging. The key point is that useful features cannot be extracted as high-quality representations for the recommender agent to optimize its policy. To tackle this problem, we propose Contrastive Representation for Interactive Recommendation (CRIR). CRIR efficiently extracts latent, high-level preference ranking features from explicit interaction, and leverages the features to enhance users' representation. Specifically, the CRIR provides representation through one representation network, and refines it through our proposed Preference Ranking Contrastive Learning (PRCL). The key insight of PRCL is that it can perform contrastive learning without relying on computations involving high-level representations or large potential action sets. Furthermore, we also propose a data exploiting mechanism and an agent training mechanism to better adapt CRIR to the DRL backbone. Extensive experiments have been carried out to show our method's superior improvement on the sample efficiency while training an DRL-based IR agent.

cs.IR↗

arXiv2024

FairSort: Learning to Fair Rank for Personalized Recommendations in Two-Sided Platforms

Traditional recommendation systems focus on maximizing user satisfaction by suggesting their favourite items. This user-centric approach may lead to unfair exposure distribution among the providers. On the contrary, a provider-centric design might become unfair to the users. Therefore, this paper proposes a re-ranking model FairSort to find a trade-off solution among user-side fairness, provider-side fairness, and personalized recommendations utility. Previous works habitually treat this issue as a knapsack problem, incorporating both-side fairness as constraints. In this paper, we adopt a novel perspective, treating each recommendation list as a runway rather than a knapsack. In this perspective, each item on the runway gains a velocity and runs within a specific time, achieving re-ranking for both-side fairness. Meanwhile, we ensure the Minimum Utility Guarantee for personalized recommendations by designing a Binary Search approach. This can provide more reliable recommendations compared to the conventional greedy strategy based on the knapsack problem. We further broaden the applicability of FairSort, designing two versions for online and offline recommendation scenarios. Theoretical analysis and extensive experiments on real-world datasets indicate that FairSort can ensure more reliable personalized recommendations while considering fairness for both the provider and user.

cs.GT↗

arXiv2020

Bayesian geoacoustic inversion using mixture density network

Bayesian geoacoustic inversion problems are conventionally solved by Markov chain Monte Carlo methods or its variants, which are computationally expensive. This paper extends the classic Bayesian geoacoustic inversion framework by deriving important geoacoustic statistics of Bayesian geoacoustic inversion from the multidimensional posterior probability density (PPD) using the mixture density network (MDN) theory. These statistics make it convenient to train the network directly on the whole parameter space and get the multidimensional PPD of model parameters. The present approach provides a much more efficient way to solve geoacoustic inversion problems in Bayesian inference framework. The network is trained on a simulated dataset of surface-wave dispersion curves with shear-wave velocities as labels and tested on both synthetic and real data cases. The results show that the network gives reliable predictions and has good generalization performance on unseen data. Once trained, the network can rapidly (within seconds) give a fully probabilistic solution which is comparable to Monte Carlo methods. It provides an promising approach for real-time inversion.

stat.ML↗