arXiv subjects

Wei Ren

Publications and source records attributed to Wei Ren.

At least 73 records · Page 4Linked to original sources

arXiv2025

Investigation on high-order planar Hall effect in trigonal PtBi$_2$

The trigonal PtBi$_2$ (t-PtBi$_2$) as a Weyl semimetal possessing triply degenerate points in its electronic bands near the Fermi level endows it with rich electronic properties. Previous studies have already measured the planar Hall effect (PHE) and in-plane anisotropic magnetoresistance (AMR) of t-PtBi$_2$. We noticed that their experimental results exhibited high-order features in both the PHE and AMR, yet these features were not systematically investigated. In our work, we conducted more systematic measurements and analyses of the PHE and AMR in t-PtBi$_2$. Both PHE and AMR show high-order features under low temperatures and strong magnetic fields, and these features share a similar temperature and magnetic field dependence with the turn-on behavior of resistance and temperature curves, indicating a common physical origin for them. We further summarize the critical conditions for the emergence of high-order PHE in t-PtBi$_2$, which will help to understand the origin of high-order features. In addition, we performed computational simulations on the AMR of t-PtBi$_2$, and the results were consistent with the experiments, indicating the high-order features are the result of the combined contribution of the Fermi surface anisotropy and the scaling behavior of magnetoresistance. Our findings will contribute to a deeper understanding of the origins of high-order features in non-magnetic topological materials.

cond-mat.mtrl-sci↗

arXiv2025

Charge density wave with suppressed long-range structural modulation in canted antiferromagnetic kagome FeGe

Kagome lattice can host abundant exotic quantum states such as superconductivity and charge density wave (CDW). Recently, successive orders of A-type antiferromagnetism (AFM), CDW and canted AFM have been manifested upon cooling in kagome FeGe. However, the mechanism of CDW and interaction with magnetism remains unclear. Here we investigate the evolution of CDW with temperature across the canted AFM by single-crystal x-ray diffraction, scanning tunneling microscope (STM) and resonant elastic x-ray scattering (REXS). Interestingly, CDW-induced superlattice reflections become weak after the canted AFM, although long-range CDW order is still detectable by STM and REXS. We uncover a novel long-range CDW order with suppressed structural modulation, likely due to the competition for the underlying crystal structure between CDW and canted AFM. Additionally, occupational modulations of Ge1 in the kagome plane and displacive modulations of all atoms were extracted. The results confirm Ge dimerization along the c axis and suggest a dynamic transformation between different CDW domains.

cond-mat.str-el↗

arXiv2025

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise

Federated Learning (FL) emerged as a solution for collaborative medical image classification while preserving data privacy. However, label noise, which arises from inter-institutional data variability, can cause training instability and degrade model performance. Existing FL methods struggle with noise heterogeneity and the imbalance in medical data. Motivated by these challenges, we propose FedGSCA, a novel framework for enhancing robustness in noisy medical FL. FedGSCA introduces a Global Sample Selector that aggregates noise knowledge from all clients, effectively addressing noise heterogeneity and improving global model stability. Furthermore, we develop a Client Adaptive Adjustment (CAA) mechanism that combines adaptive threshold pseudo-label generation and Robust Credal Labeling Loss. CAA dynamically adjusts to class distributions, ensuring the inclusion of minority samples and carefully managing noisy labels by considering multiple plausible labels. This dual approach mitigates the impact of noisy data and prevents overfitting during local training, which improves the generalizability of the model. We evaluate FedGSCA on one real-world colon slides dataset and two synthetic medical datasets under various noise conditions, including symmetric, asymmetric, extreme, and heterogeneous types. The results show that FedGSCA outperforms the state-of-the-art methods, excelling in extreme and heterogeneous noise scenarios. Moreover, FedGSCA demonstrates significant advantages in improving model stability and handling complex noise, making it well-suited for real-world medical federated learning scenarios.

cs.LG↗

arXiv2025

The interplay of ferroelectricity and magneto-transport in non-magnetic moiré superlattices

The coupling of ferroelectricity and magnetic order provides rich tunability for engineering material properties and demonstrates great potential for uncovering novel quantum phenomena and multifunctional devices. Here, we report interfacial ferroelectricity in moiré superlattices constructed from graphene and hexagonal boron nitride. We observe ferroelectric polarization in an across-layer moiré superlattice with an intercalated layer, demonstrating a remnant polarization comparable to its non-intercalated counterpart. Remarkably, we reveal a magnetic-field enhancement of ferroelectric polarization that persists up to room temperature, showcasing an unconventional amplification of ferroelectricity in materials lacking magnetic elements. This phenomenon, consistent across devices with varying layer configurations, arises purely from electronic rather than ionic contributions. Furthermore, the ferroelectric polarization in turn modulates quantum transport characteristics, suppressing Shubnikov-de Haas oscillations and altering quantum Hall states in polarized phases. This interplay between ferroelectricity and magneto-transport in non-magnetic materials is crucial for exploring magnetoelectric effects and advancing two-dimensional memory and logic applications.

cond-mat.mtrl-sci↗

arXiv2025

Ascent and descent of Gorenstein homological properties

Let $φ\colon R\rightarrow A$ be a ring homomorphism, where $R$ is a commutative noetherian ring and $A$ is a finite $R$-algebra. We provide criteria for detecting the ascent and descent of Gorenstein homological properties. %As an application, one can deduce a result that supports a question of Avramov and Foxby. We observe that the ascent and descent of Gorenstein homological property can detect the Gorenstein properties of rings along $φ$. Finally, we describe when $φ$ induces a triangle equivalence between the stable categories of finitely generated Gorenstein projective modules over $R$ and $A$.

math.AC↗

arXiv2025

Distributed Neural Policy Gradient Algorithm for Global Convergence of Networked Multi-Agent Reinforcement Learning

This paper studies the networked multi-agent reinforcement learning (NMARL) problem, where the objective of agents is to collaboratively maximize the discounted average cumulative rewards. Different from the existing methods that suffer from poor expression due to linear function approximation, we propose a distributed neural policy gradient algorithm that features two innovatively designed neural networks, specifically for the approximate Q-functions and policy functions of agents. This distributed neural policy gradient algorithm consists of two key components: the distributed critic step and the decentralized actor step. In the distributed critic step, agents receive the approximate Q-function parameters from their neighboring agents via a time-varying communication networks to collaboratively evaluate the joint policy. In contrast, in the decentralized actor step, each agent updates its local policy parameter solely based on its own approximate Q-function. In the convergence analysis, we first establish the global convergence of agents for the joint policy evaluation in the distributed critic step. Subsequently, we rigorously demonstrate the global convergence of the overall distributed neural policy gradient algorithm with respect to the objective function. Finally, the effectiveness of the proposed algorithm is demonstrated by comparing it with a centralized algorithm through simulation in the robot path planning environment.

cs.MA↗

arXiv2025

Contextualized Automatic Speech Recognition with Dynamic Vocabulary Prediction and Activation

Deep biasing improves automatic speech recognition (ASR) performance by incorporating contextual phrases. However, most existing methods enhance subwords in a contextual phrase as independent units, potentially compromising contextual phrase integrity, leading to accuracy reduction. In this paper, we propose an encoder-based phrase-level contextualized ASR method that leverages dynamic vocabulary prediction and activation. We introduce architectural optimizations and integrate a bias loss to extend phrase-level predictions based on frame-level outputs. We also introduce a confidence-activated decoding method that ensures the complete output of contextual phrases while suppressing incorrect bias. Experiments on Librispeech and Wenetspeech datasets demonstrate that our approach achieves relative WER reductions of 28.31% and 23.49% compared to baseline, with the WER on contextual phrases decreasing relatively by 72.04% and 75.69%.

cs.SD↗

arXiv2025

Group class operations and homological conditions

Kropholler's operation ${\scriptstyle{\bf LH}}$ and Talelli's operation $Φ$ can be often used to formally enlarge the class of available examples of groups that satisfy certain homological conditions. In this paper, we employ this enlargement technique regarding two specific homological conditions. We thereby demonstrate the abundance of groups that (a) have virtually Gorenstein group algebras, as defined by Beligiannis and Reiten, and (b) satisfy Moore's conjecture on the relation between projectivity and relative projectivity, that was studied by Chouinard, Aljadeff, Cornick, Ginosar, Kropholler and Meir.

math.GR↗

arXiv2025

On certain classes of modules over group algebras

In this paper, we examine the relation between certain subclasses of the classes of Gorenstein projective, Gorenstein flat and Gorenstein injective modules over a group algebra, which consist of the cofibrant, cofibrant-flat and fibrant modules respectively. These subclasses have all structural properties that the Gorenstein classes are known to have, regarding the existence of complete cotorsion pairs and various approximations between them. Furthermore, these subclasses have certain properties, which are conjecturally anticipated to hold for the Gorenstein classes as well. Since the Gorenstein classes are actually equal to the corresponding subclasses for large families of groups, we thus obtain (indirectly) some information about the Gorenstein classes over these groups.

math.KT↗

arXiv2025

Cup Products on Hochschild Cohomology of Hopf-Galois Extensions.pdf

In this paper, we give an explicit chain map, which induces the algebra isomorphism between the Hochschild cohomology ${\bf HH}^{\bullet}(B)$ and the $H$-invariant subalgebra ${\bf H}^{\bullet}(A, B)^{H}$ under two mild hypotheses, where $H$ is a finite dimensional semisimple Hopf algebra and $B$ is an $H$-Galois extension of $A$. In particular, the smash product $B=A\#H$ always satisfies the mild hypotheses. The isomorphism between ${\bf HH}^{\bullet}(A\#H)$ and ${\bf H}^{\bullet}(A, A\#H)^{H}$ generalizes the classical result of group actions. As an application, Hochschild cohomology and cup product of the smash product of the quantum $(-1)$-plane and Kac--Paljutkin Hopf algebra are computed.

math.RA↗

arXiv2025

Observation of superconducting pair density modulation within lattice unit cell

In unconventional high-temperature (high-Tc) superconductors, the pair density wave state, an exotic superconducting order showing spatially periodic order parameter modulations with the period of several unit cells and translational symmetry breaking, has attracted broad attention. However, the superconducting pair density modulation within a single unit cell (PDM) has never been carefully investigated before. Here, using scanning tunneling microscopy/spectroscopy, we report the observation of PDM in monolayer high-Tc iron chalcogenide films epitaxially grown on SrTiO3(001). The superconductivity modulations are characterized by the superconducting gap size and the coherence peak sharpness. Further analysis shows that the local maxima and minima in the superconducting gap modulation are centered at the crystallographic locations of the chalcogen atoms, revealing the breaking of the glide-mirror symmetry of the chalcogen atoms in monolayer high-Tc iron chalcogenide films grown on SrTiO3(001). Our findings provide precise microscopic information on superconductivity within the lattice unit cell and may promote the understanding of unconventional high-Tc superconductivity.

cond-mat.supr-con↗

arXiv2024

Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing

Analog computing using non-volatile memristors has emerged as a promising solution for energy-efficient deep learning. New materials, like perovskites-based memristors are recently attractive due to their cost-effectiveness, energy efficiency and flexibility. Yet, challenges in material diversity and immature fabrications require extensive experimentation for device development. Moreover, significant non-idealities in these memristors often impede them for computing. Here, we propose a synergistic methodology to concurrently optimize perovskite memristor fabrication and develop robust analog DNNs that effectively address the inherent non-idealities of these memristors. Employing Bayesian optimization (BO) with a focus on usability, we efficiently identify optimal materials and fabrication conditions for perovskite memristors. Meanwhile, we developed "BayesMulti", a DNN training strategy utilizing BO-guided noise injection to improve the resistance of analog DNNs to memristor imperfections. Our approach theoretically ensures that within a certain range of parameter perturbations due to memristor non-idealities, the prediction outcomes remain consistent. Our integrated approach enables use of analog computing in much deeper and wider networks, which significantly outperforms existing methods in diverse tasks like image classification, autonomous driving, species identification, and large vision-language models, achieving up to 100-fold improvements. We further validate our methodology on a 10$\times$10 optimized perovskite memristor crossbar, demonstrating high accuracy in a classification task and low energy consumption. This study offers a versatile solution for efficient optimization of various analog computing systems, encompassing both devices and algorithms.

cs.LG↗

arXiv2024

Large Language Models Merging for Enhancing the Link Stealing Attack on Graph Neural Networks

Graph Neural Networks (GNNs), specifically designed to process the graph data, have achieved remarkable success in various applications. Link stealing attacks on graph data pose a significant privacy threat, as attackers aim to extract sensitive relationships between nodes (entities), potentially leading to academic misconduct, fraudulent transactions, or other malicious activities. Previous studies have primarily focused on single datasets and did not explore cross-dataset attacks, let alone attacks that leverage the combined knowledge of multiple attackers. However, we find that an attacker can combine the data knowledge of multiple attackers to create a more effective attack model, which can be referred to cross-dataset attacks. Moreover, if knowledge can be extracted with the help of Large Language Models (LLMs), the attack capability will be more significant. In this paper, we propose a novel link stealing attack method that takes advantage of cross-dataset and Large Language Models (LLMs). The LLM is applied to process datasets with different data structures in cross-dataset attacks. Each attacker fine-tunes the LLM on their specific dataset to generate a tailored attack model. We then introduce a novel model merging method to integrate the parameters of these attacker-specific models effectively. The result is a merged attack model with superior generalization capabilities, enabling effective attacks not only on the attackers' datasets but also on previously unseen (out-of-domain) datasets. We conducted extensive experiments in four datasets to demonstrate the effectiveness of our method. Additional experiments with three different GNN and LLM architectures further illustrate the generality of our approach.

cs.CR↗

arXiv2024

Gorenstein cohomological dimension and stable categories for groups

First we study the Gorenstein cohomological dimension ${\rm Gcd}_RG$ of groups $G$ over coefficient rings $R$, under changes of groups and rings; a characterization for finiteness of ${\rm Gcd}_RG$ is given. Some results in literature obtained over the coefficient ring $\mathbb{Z}$ or rings of finite global dimension are generalized to more general cases. Moreover, we establish a model structure on the weakly idempotent complete exact category $\mathcal{F}ib$ consisting of fibrant $RG$-modules, and show that the homotopy category $\mathrm{Ho}(\mathcal{F}ib)$ is triangle equivalent to both the stable category $\underline{\mathcal{C}of}(RG)$ of Benson's cofibrant modules, and the stable module category ${\rm StMod}(RG)$. The relation between cofibrant modules and Gorenstein projective modules is discussed, and we show that under some conditions such that ${\rm Gcd}_RG<\infty$, ${\rm Ho}(\mathcal{F}ib)$ is equivalent to the stable category of Gorenstein projective $RG$-modules, the singularity category, and the homotopy category of totally acyclic complexes of projective $RG$-modules.

math.KT↗

arXiv2024

Noncontact Multi-Point Vital Sign Monitoring with mmWave MIMO Radar

Multi-point vital sign monitoring is essential for providing detailed insights into physiological changes. Traditional single-sensor approaches are inadequate for capturing multi-point vibrations. Existing contact-based solutions, while addressing this need, can cause discomfort and skin allergies, whereas noncontact optical and acoustic methods are highly susceptible to light interference and environmental noise. In this paper, we aim to develop a non-contact, multi-point vital sign monitoring technique using MIMO radar, focused on physically differentiating and precisely measuring chest-wall surface vibrations at multiple points induced by cardiopulmonary mechanical activity. The primary challenges in developing such a technique involve developing algorithms to extract and separate entangled signals, as well as establishing a reliable method for validating detection accuracy. To address these limitations, we introduce MultiVital, a wireless system that leverages mmWave Multiple-input Multiple-output (MIMO) radar for synchronous multi-point vital sign monitoring. It integrates two reference modalities: five-channel seismocardiography (SCG) sensors and a one-channel electrocardiogram (ECG) electrode, enabling comprehensive radar-based research and performance validation across multiple physiological metrics. Additionally, we have developed a multi-modal signal processing framework, consisting of a radar signal processing module, an SCG calibration module, and a spatial alignment scheme. To evaluate the radar signal processing module, we conducted mathematical derivation and simulation. The experimental results indicate that the noncontact MultiVital system achieves multi-point synchronous monitoring with high precision, highly consistent with the results from reference modalities.

cs.AR↗

arXiv2024

Crystalline electric field excitations and their nonlinear splitting under magnetic fields in YbOCl

Recently reported van der Waals layered honeycomb rare-earth chalcohalides REChX (RE = rare earth, Ch = chalcogen, and X = halogen) are considered to be promising Kitaev spin liquid (KSL) candidates. The high-quality single crystals of YbOCl, a representative member of the family with an effective spin of 1/2, are available now. The crystalline electric field (CEF) excitations in a rare-earth spin system are fundamentally important for understanding both finite-temperature and ground-state magnetism, but remain unexplored in YbOCl so far. In this paper, we conduct a comprehensive Raman scattering study to unambiguously identify the CEF excitations in YbOCl and determine the CEF parameters and wave functions. Our Raman experiments further reveal the anomalous nonlinear CEF splitting under magnetic fields. We have grown single crystals of YbOCl, the nonmagnetic LuOCl, and the diluted magnetic Lu_{0.86}Yb_{0.14}OCl to make a completely comparative investigation. Polarized Raman spectra on the samples at 1.8 K allow us to clearly assign all the Raman-active phonon modes and explicitly identify the CEF excitations in YbOCl. The CEF excitations are further examined using temperature-dependent Raman measurements and careful symmetry analysis based on Raman tensors related to CEF excitations. By applying the CEF Hamiltonian to the experimentally determined CEF excitations, we extract the CEF parameters and eventually determine the CEF wave functions. The study experimentally pins down the CEF excitations in the Kitaev compound YbOCl and sets a foundation for understanding its finite-temperature magnetism and exploring the possible nontrivial spin ground state.

cond-mat.str-el↗

arXiv2024

Ideal-LLM: Integrating Dual Encoders and Language-Adapted LLM for Multilingual Speech-to-Text

Integrating audio encoders with LLMs through connectors has enabled these models to process and comprehend audio modalities, significantly enhancing speech-to-text tasks, including automatic speech recognition (ASR) and automatic speech translation (AST). However, these methods often overlook the critical aspect of language adaptation in multilingual settings, relying instead on multilingual data without adequately addressing language differences. To address this gap, we propose the Ideal-LLM model, which employs dual multilingual encoders to enrich language feature information and utilizes a language-adapted connector to target the adaptation of each language specifically. By leveraging the complementary strengths of Whisper and MMS encoders, our approach ensures richer multilingual representations. Additionally, the language-adapted connector enhances modal transformation via a language weight selector tailored for each language. Experimental results demonstrate that Ideal-LLM significantly improves ASR performance, achieving a 32.6% relative reduction in average word error rates compared to the standard speech encoder integrated with LLMs and yields an average BLEU score of 36.78 for AST task.

eess.AS↗

arXiv2024

Robust Coulomb Gap and Varied-temperature Study of Epitaxial 1T'-WSe$_2$ Monolayers

The transition metal dichalcogenides (TMDCs) with a 1T' structural phase are predicted to be two-dimensional topological insulators at zero temperature. Although the quantized edge conductance of 1T'-WTe$_2$ has been confirmed to survive up to 100 K, this temperature is still relatively low for industrial applications. Addressing the limited studies on temperature effects in 1T'-TMDCs, our research focuses on the electronic and crystal properties of the epitaxial 1T'-WSe$_2$ monolayers grown on bilayer graphene (BLG) and SrTiO$_3$(100) substrates at various temperatures. For the 1T'-WSe$_2$ grown on BLG, we observed a significant thermal expansion effect on its band structures with a thermal expansion coefficient of $\sim$60$\times$10$^{-6}$ K$^{-1}$. In contrast, the 1T'-WSe$_2$ grown on SrTiO$_3$(100) exhibits minimal changes with varied temperatures due to the enhanced strain exerted by the substrate. Besides, A significant Coulomb gap (CG) was observed pinned at the Fermi level in the angle-resolved photoemission spectroscopy (ARPES) and scanning tunneling spectroscopy (STS). The CG was founded to decrease with increasing temperatures, and can persist up to 200 K for 1T'-WSe$_2$/BLG, consistent with our Monte Carlo simulations. The robustness of the CG and the positive fundamental gap endow the epitaxial 1T'-WSe$_2$ monolayers with huge potential for realizing the quantum spin Hall devices.

cond-mat.mes-hall↗