arXiv subjects

Chen Lu

Publications and source records attributed to Chen Lu.

At least 37 records · Page 2Linked to original sources

arXiv2025

Classification of Magnetism and Altermagnetism in Quasicrystals

Altermagnetism (AM), an unconventional magnetic phase characterized by zero net magnetism protected by symmetry(s) other than parity-time ($\mathcal{P}\mathcal{T}$) and a resulting spin-split band, has been studied exclusively in crystalline materials. Here, we extend the framework of AM to quasicrystals (QCs). We start from a comparison between the Néel state on the square lattice and that on a $D_4$-symmetric Thue-Morse QC, with both belonging to the same $d$-wave irreducible representation (IRRP) of the $D_4$ point group. Consequently, while the former is antiferromagnetism (AFM) protected by the combined $\mathcal{P}\mathcal{T}$ and translational symmetry, the lack of translational symmetry in the latter breaks the $\mathcal{P}\mathcal{T}$ symmetry, and the additional mirror or rotation symmetry protects AM. This example suggests that AM is more common in QCs than in crystals and can be easily explored through a point-group symmetry-based classification. Therefore, we classify magnetic phases in 2D $D_n$-symmetric QCs without spin-orbit coupling, by using IRRPs of $D_n$. Consequently, the identity IRRP represents ferromagnetism, the inversion-odd 1D IRRPs for twice-of-odd $n$ represent AFM, and all the remaining 1D IRRPs represent AM, protected by either mirror or rotation symmetry. We further take the Hubbard model to verify this result in various QCs with different symmetries. Our work highlights the QC as a natural platform where AM is common among magnetic phases.

cond-mat.str-el↗

arXiv2025

PHM-Bench: A Domain-Specific Benchmarking Framework for Systematic Evaluation of Large Models in Prognostics and Health Management

With the rapid advancement of generative artificial intelligence, large language models (LLMs) are increasingly adopted in industrial domains, offering new opportunities for Prognostics and Health Management (PHM). These models help address challenges such as high development costs, long deployment cycles, and limited generalizability. However, despite the growing synergy between PHM and LLMs, existing evaluation methodologies often fall short in structural completeness, dimensional comprehensiveness, and evaluation granularity. This hampers the in-depth integration of LLMs into the PHM domain. To address these limitations, this study proposes PHM-Bench, a novel three-dimensional evaluation framework for PHM-oriented large models. Grounded in the triadic structure of fundamental capability, core task, and entire lifecycle, PHM-Bench is tailored to the unique demands of PHM system engineering. It defines multi-level evaluation metrics spanning knowledge comprehension, algorithmic generation, and task optimization. These metrics align with typical PHM tasks, including condition monitoring, fault diagnosis, RUL prediction, and maintenance decision-making. Utilizing both curated case sets and publicly available industrial datasets, our study enables multi-dimensional evaluation of general-purpose and domain-specific models across diverse PHM tasks. PHM-Bench establishes a methodological foundation for large-scale assessment of LLMs in PHM and offers a critical benchmark to guide the transition from general-purpose to PHM-specialized models.

cs.AI↗

arXiv2025

Pairing without $γ$-Pocket in the La$_3$Ni$_2$O$_7$ Thin Film

The recent discovery of high-temperature superconductivity (HTSC) in the La$_3$Ni$_2$O$_7$ ultrathin film at ambient pressure has aroused great research interest. The $γ$-pocket formed by the bonding $d_{z^2}$ band, which was previously proposed to be crucial in the pairing mechanism of pressurized bulk La$_3$Ni$_2$O$_7$, is reported to be either present or absent here by different experimental groups, giving rise to the problem: what is the pairing mechanism and pairing nature without the $γ$-pocket? Here, we start from a band structure obtained via density-functional-theoretical calculation, which exhibits no $γ$-pocket. Then, equipped with electron interactions, we study the pairing nature via combined weak- and strong- coupling approaches, which provide consistent results. In the weak-coupling study, the nesting between the $α$- and $β$- pockets leads to an $s^\pm$-wave pairing in which the gap signs on the two pockets are opposite, as provided by our random-phase-approximation based calculations. In real-space, the pairing pattern is dominated by the interlayer pairing of the $d_{x^2-y^2}$ orbital. In the strong-coupling study, as the $d_{z^2}$ orbitals are nearly half-filled and hence localized, the $d_{x^2-y^2}$ orbitals carry the HTSC. Driven by the interlayer superexchange transferred from the $d_{z^2}$ orbital through the Hund's rule coupling, the $d_{x^2-y^2}$ orbital electrons form interlayer $s$-wave pairing, as suggested by our slave-boson-mean-field study on the related two-orbital $t$-$J$ model. Projected onto the Fermi surface, this pairing just gives the $s^\pm$-wave pattern consistent with that obtained in the weak-coupling study. Our result is consistent with that obtained in recent scanning tunneling microscopy experiment.

cond-mat.supr-con↗

arXiv2025

Octupolar Weyl Superconductivity from Electron-electron Interaction

Unconventional superconductivity arising from electron-electron interaction can manifest exotic symmetry and topological properties. We investigate the superconducting pairing symmetry problem based on the 3D cubic $O_h$ symmetry with both weak- and strong-coupling approaches. The dominant pairing symmetries belong to the two-dimensional $E_g$ representation at low and intermediate doping levels, and the complex mixing gap function of the $d_{3z^2-r^2}+id_{x^2-y^2}$-type is energetically favored in the ground state. Cooper pairs with such a symmetry do not possess orbital angular momentum (OAM) moments, which is different from other time-reversal symmetry breaking pairings such as $p_x+ip_y$ (e.g $^3$He-A) and $d_{x^2-y^2}+id_{xy}$ under the planar hexagonal symmetry. Instead, they develop the octupolar $O_{xyz}$ component of OAM, which results in 8 nodal points along the body diagonal directions exhibiting an alternating distribution of monopole charges $\pm 1$. This leads to an intriguing 3D Weyl topological SC, which accommodates nontrivial surface states of Majorana arcs. Our results appeal for material realizations and experimental tests in optical lattices.

cond-mat.supr-con↗

arXiv2025

Fractional order derivative characterizations of Besov-Morrey type spaces with applications

On the one hand, the fractional order derivative characterization of the Besov-Morrey type space $B_{p}^{K}(s)$ is established by $K$-Carleson measures, and it was also shown that $f \in B_{p}^{K}(s_1) \Leftrightarrow f^{\left(\frac{s_2 - s_1}{p}\right)} \in B_{p}^{K}(s_2)$, which extended the results of Sun et al. on the fractional derivative of Morrey type space. On the other hand, some sufficient conditions for the growth of solutions to linear complex differential equations have been obtained by using $n$th derivative criterion.

math.CV↗

arXiv2025

Superconductivity in La$_4$Ni$_3$O$_{10}$ Under Pressure

The discovery of superconductivity (SC) in the trilayer nickelate compound La$_{4}$Ni$_3$O$_{10}$ under pressure has generated significant interest. In this work, we propose a trilayer two $E_g$-orbital $t$-$J_{\parallel}$-$J_{\perp}$ model to investigate the microscopic origin of SC in this system. In the strong-coupling regime, each layer is governed by a $t$-$J_{\parallel}$ model with intra-layer antiferromagnetic exchange $J_{\parallel}$, while electrons are allowed to hop between layers, interacting via inter-layer exchange $J_{\perp}$. The inner-layer $3d_{z^2}$-orbital electrons tends to form bonding states with those in the neighboring layers, leading to redistribution of the electron densities. The numerical simulation results indicate that SC is predominantly mediated by the $3d_{z^2}$ orbital, characterized by an intra-layer extended $s$-wave pairing in the outer layers, accompanied by an inter-layer pairing with opposite sign. Furthermore, we find that electron doping enhances SC, while hole doping tends to suppress it. These findings provide new insights into the SC mechanisms of La$_{4}$Ni$_3$O$_{10}$ and its sensitivity to charge doping.

cond-mat.supr-con↗

arXiv2025

Pre-Trained Large Language Model Based Remaining Useful Life Transfer Prediction of Bearing

Accurately predicting the remaining useful life (RUL) of rotating machinery, such as bearings, is essential for ensuring equipment reliability and minimizing unexpected industrial failures. Traditional data-driven deep learning methods face challenges in practical settings due to inconsistent training and testing data distributions and limited generalization for long-term predictions.

eess.SY↗

arXiv2024

LLM-R: A Framework for Domain-Adaptive Maintenance Scheme Generation Combining Hierarchical Agents and RAG

The increasing use of smart devices has emphasized the critical role of maintenance in production activities. Interactive Electronic Technical Manuals (IETMs) are vital tools that support the maintenance of smart equipment. However, traditional IETMs face challenges such as transitioning from Graphical User Interfaces (GUIs) to natural Language User Interfaces (LUIs) and managing complex logical relationships. Additionally, they must meet the current demands for higher intelligence. This paper proposes a Maintenance Scheme Generation Method based on Large Language Models (LLM-R). The proposed method includes several key innovations: We propose the Low Rank Adaptation-Knowledge Retention (LORA-KR) loss technology to proportionally adjust mixed maintenance data for fine-tuning the LLM. This method prevents knowledge conflicts caused by mixed data, improving the model's adaptability and reasoning ability in specific maintenance domains, Besides, Hierarchical Task-Based Agent and Instruction-level Retrieval-Augmented Generation (RAG) technologies are adopted to optimize the generation steps and mitigate the phenomenon of hallucination caused by the model's Inability to access contextual information. This enhancement improves the model's flexibility and accuracy in handling known or unknown maintenance objects and maintenance scheme scenarios. To validate the proposed method's effectiveness in maintenance tasks, a maintenance scheme dataset was constructed using objects from different fields. The experimental results show that the accuracy of the maintenance schemes generated by the proposed method reached 91.59%, indicating which improvement enhances the intelligence of maintenance schemes and introduces novel technical approaches for equipment maintenance.

cs.LG↗

arXiv2024

LLM-based Framework for Bearing Fault Diagnosis

Accurately diagnosing bearing faults is crucial for maintaining the efficient operation of rotating machinery. However, traditional diagnosis methods face challenges due to the diversification of application environments, including cross-condition adaptability, small-sample learning difficulties, and cross-dataset generalization. These challenges have hindered the effectiveness and limited the application of existing approaches. Large language models (LLMs) offer new possibilities for improving the generalization of diagnosis models. However, the integration of LLMs with traditional diagnosis techniques for optimal generalization remains underexplored. This paper proposed an LLM-based bearing fault diagnosis framework to tackle these challenges. First, a signal feature quantification method was put forward to address the issue of extracting semantic information from vibration data, which integrated time and frequency domain feature extraction based on a statistical analysis framework. This method textualized time-series data, aiming to efficiently learn cross-condition and small-sample common features through concise feature selection. Fine-tuning methods based on LoRA and QLoRA were employed to enhance the generalization capability of LLMs in analyzing vibration data features. In addition, the two innovations (textualizing vibration features and fine-tuning pre-trained models) were validated by single-dataset cross-condition and cross-dataset transfer experiment with complete and limited data. The results demonstrated the ability of the proposed framework to perform three types of generalization tasks simultaneously. Trained cross-dataset models got approximately a 10% improvement in accuracy, proving the adaptability of LLMs to input patterns. Ultimately, the results effectively enhance the generalization capability and fill the research gap in using LLMs for bearing fault diagnosis.

eess.SP↗

arXiv2024

Confidence Self-Calibration for Multi-Label Class-Incremental Learning

The partial label challenge in Multi-Label Class-Incremental Learning (MLCIL) arises when only the new classes are labeled during training, while past and future labels remain unavailable. This issue leads to a proliferation of false-positive errors due to erroneously high confidence multi-label predictions, exacerbating catastrophic forgetting within the disjoint label space. In this paper, we aim to refine multi-label confidence calibration in MLCIL and propose a Confidence Self-Calibration (CSC) approach. Firstly, for label relationship calibration, we introduce a class-incremental graph convolutional network that bridges the isolated label spaces by constructing learnable, dynamically extended label relationship graph. Then, for confidence calibration, we present a max-entropy regularization for each multi-label increment, facilitating confidence self-calibration through the penalization of over-confident output distributions. Our approach attains new state-of-the-art results in MLCIL tasks on both MS-COCO and PASCAL VOC datasets, with the calibration of label confidences confirmed through our methodology.

cs.CV↗

arXiv2024

RSL-BA: Rolling Shutter Line Bundle Adjustment

The line is a prevalent element in man-made environments, inherently encoding spatial structural information, thus making it a more robust choice for feature representation in practical applications. Despite its apparent advantages, previous rolling shutter bundle adjustment (RSBA) methods have only supported sparse feature points, which lack robustness, particularly in degenerate environments. In this paper, we introduce the first rolling shutter line-based bundle adjustment solution, RSL-BA. Specifically, we initially establish the rolling shutter camera line projection theory utilizing Plücker line parameterization. Subsequently, we derive a series of reprojection error formulations which are stable and efficient. Finally, we theoretically and experimentally demonstrate that our method can prevent three common degeneracies, one of which is first discovered in this paper. Extensive synthetic and real data experiments demonstrate that our method achieves efficiency and accuracy comparable to existing point-based rolling shutter bundle adjustment solutions.

cs.CV↗

arXiv2024

Enhancing Complex Causality Extraction via Improved Subtask Interaction and Knowledge Fusion

Event Causality Extraction (ECE) aims at extracting causal event pairs from texts. Despite ChatGPT's recent success, fine-tuning small models remains the best approach for the ECE task. However, existing fine-tuning based ECE methods cannot address all three key challenges in ECE simultaneously: 1) Complex Causality Extraction, where multiple causal-effect pairs occur within a single sentence; 2) Subtask~ Interaction, which involves modeling the mutual dependence between the two subtasks of ECE, i.e., extracting events and identifying the causal relationship between extracted events; and 3) Knowledge Fusion, which requires effectively fusing the knowledge in two modalities, i.e., the expressive pretrained language models and the structured knowledge graphs. In this paper, we propose a unified ECE framework (UniCE to address all three issues in ECE simultaneously. Specifically, we design a subtask interaction mechanism to enable mutual interaction between the two ECE subtasks. Besides, we design a knowledge fusion mechanism to fuse knowledge in the two modalities. Furthermore, we employ separate decoders for each subtask to facilitate complex causality extraction. Experiments on three benchmark datasets demonstrate that our method achieves state-of-the-art performance and outperforms ChatGPT with a margin of at least 30% F1-score. More importantly, our model can also be used to effectively improve the ECE performance of ChatGPT via in-context learning.

cs.CL↗

arXiv2024

An Outline of Prognostics and Health Management Large Model: Concepts, Paradigms, and Challenges

Prognosis and Health Management (PHM), critical for ensuring task completion by complex systems and preventing unexpected failures, is widely adopted in aerospace, manufacturing, maritime, rail, energy, etc. However, PHM's development is constrained by bottlenecks like generalization, interpretation and verification abilities. Presently, generative artificial intelligence (AI), represented by Large Model, heralds a technological revolution with the potential to fundamentally reshape traditional technological fields and human production methods. Its capabilities, including strong generalization, reasoning, and generative attributes, present opportunities to address PHM's bottlenecks. To this end, based on a systematic analysis of the current challenges and bottlenecks in PHM, as well as the research status and advantages of Large Model, we propose a novel concept and three progressive paradigms of Prognosis and Health Management Large Model (PHM-LM) through the integration of the Large Model with PHM. Subsequently, we provide feasible technical approaches for PHM-LM to bolster PHM's core capabilities within the framework of the three paradigms. Moreover, to address core issues confronting PHM, we discuss a series of technical challenges of PHM-LM throughout the entire process of construction and application. This comprehensive effort offers a holistic PHM-LM technical framework, and provides avenues for new PHM technologies, methodologies, tools, platforms and applications, which also potentially innovates design, research & development, verification and application mode of PHM. And furthermore, a new generation of PHM with AI will also capably be realized, i.e., from custom to generalized, from discriminative to generative, and from theoretical conditions to practical applications.

cs.AI↗

arXiv2024

Frustrated superconductivity and sextetting order

The superconducting state typically favors a uniform spatial distribution akin to ferromagnetism. Nevertheless, the pair-density-wave state exhibits sign changes in the pairing order, leading to potential frustrations in phase coherence.We propose a mechanism to the sextetting order stemming from the frustrations in the phase coherence of a pair-density-wave state, whose spatial modulation manifests a vortex-antivortex honeycomb lattice. The classical ground state configurations are mapped to Baxter's three-coloring model, revealing a macroscopic degeneracy accompanied by extensive entropy. The phase coherence problem intertwines the U(1) phases and the vorticity variables. While the resultant color and phase fluctuations suppress the pair-density-wave order, they maintain the sextetting order above the superconducting transition temperature ($T_{\text{c}}$). The $1/3$-fractional vortex emerges as the fundamental topological defect in the sextetting order. This novel mechanism of frustrated superconductivity provides an alternative explanation for the experimental observed fractional oscillations in CsV$_3$Sb$_5$.

cond-mat.supr-con↗

arXiv2024

Interlayer Coupling Driven High-Temperature Superconductivity in La$_3$Ni$_2$O$_7$ Under Pressure

The newly discovered high-temperature superconductivity in La$_3$Ni$_2$O$_7$ under pressure has attracted a great deal of attentions. The essential ingredient characterizing the electronic properties is the bilayer NiO$_2$ planes coupled by the interlayer bonding of $3d_{z^2}$ orbitals through the intermediate oxygen-atoms. In the strong coupling limit, the low energy physics is described by an intralayer antiferromagnetic spin-exchange interaction $J_{\parallel}$ between $3d_{x^2-y^2}$ orbitals and an interlayer one $J_{\perp}$ between $3d_{z^2}$ orbitals. Taking into account Hund's rule on each site and integrating out the $3d_{z^2}$ spin degree of freedom, the system reduces to a single-orbital bilayer $t$-$J$ model based on the $3d_{x^2-y^2}$ orbital. By employing the slave-boson approach, the self-consistent equations for the bonding and pairing order parameters are solved. Near the physically relevant $\frac{1}{4}$-filling regime (doping $δ=0.3\sim 0.5$), the interlayer coupling $J_{\perp}$ tunes the conventional single-layer $d$-wave superconducting state to the $s$-wave one. A strong $J_{\perp}$ could enhance the inter-layer superconducting order, leading to a dramatically increased $T_c$. Interestingly, there could exist a finite regime in which an $s+id$ state emerges.

cond-mat.supr-con↗

arXiv2024

AI Empowered Channel Semantic Acquisition for 6G Integrated Sensing and Communication Networks

Motivated by the need for increased spectral efficiency and the proliferation of intelligent applications, the sixth-generation (6G) mobile network is anticipated to integrate the dual-functions of communication and sensing (C&S). Although the millimeter wave (mmWave) communication and mmWave radar share similar multiple-input multiple-output (MIMO) architecture for integration, the full potential of dual-function synergy remains to be exploited. In this paper, we commence by overviewing state-of-the-art schemes from the aspects of waveform design and signal processing. Nevertheless, these approaches face the dilemma of mutual compromise between C&S performance. To this end, we reveal and exploit the synergy between C&S. In the proposed framework, we introduce a two-stage frame structure and resort artificial intelligence (AI) to achieve the synergistic gain by designing a joint C&S channel semantic extraction and reconstruction network (JCASCasterNet). With just a cost-effective and energy-efficient single sensing antenna, the proposed scheme achieves enhanced overall performance while requiring only limited pilot and feedback signaling overhead. In the end, we outline the challenges that lie ahead in the future development of integrated sensing and communication networks, along with promising directions for further research.

cs.IT↗

arXiv2023

Hydrogen Doping Induced $p_x\pm ip_y$ Triplet Superconductivity in Quasi-One-Dimensional K$_2$Cr$_3$As$_3$

Quasi-one-dimensional (Q1D) Cr-based pnictide K$_2$Cr$_3$As$_3$ has aroused great research interest due to its possible triplet superconducting pairing symmetry. Recent experiments have shown that incorporating hydrogen atoms into K$_2$Cr$_3$As$_3$ would significantly change its electronic and magnetic properties. Hence, it's necessary to investigate the impact of hydrogen doping in superconducting pairing symmetry of this material. Employing the hydrogen as an non-trivial electron-doping, our calculates show that, different from the $p_z$-wave obtained without hydrogen, the system exhibits $p_x\pm ip_y$ pairing superconductivity under specific hydrogen doping. Specifically, we adopt the random-phase-approximation approach based on a six-band tight-binding model equipped with multi-orbital Hubbard interactions to study the hydrogen-doping dependence of the pairing symmetry and superconducting $T_c$. Under the rigid-band approximation, our pairing phase diagram shows the spin-triplet pairing states is dominated through out the hydrogen-doping regime $x\in (0,0.7)$. Particularly, the $T_c\sim x$ curve shows a peak at the 3D-quasi-1D Lifshitz transition point, and the pairing symmetry around this doping level is $p_x\pm ip_y$. The physical origin of this pairing symmetry is that the density of states is mainly concentrated at $k_x(k_y)$ with large momentum. Due to the three-dimensional character of the real material, this $p_x\pm ip_y$-wave superconducting state possesses point gap nodes. We further provide experiment prediction to identify this triplet $p_x\pm ip_y$-wave superconductivity.

cond-mat.supr-con↗

arXiv2023

Interplay of two $E_g$ orbitals in Superconducting La$_3$Ni$_2$O$_7$ Under Pressure

The discovery of high-$T_c$ superconductivity (SC) in La$_3$Ni$_2$O$_7$ (LNO) has aroused a great deal of interests. Previously, it was proposed that the Ni-$3d_{z^2}$ orbital is crucial to realize the high-$T_c$ SC in LNO: The preformed Cooper pairs therein acquire coherence via hybridization with the $3d_{x^2-y^2}$ orbital to form the SC. However, we held a different viewpoint that the interlayer pairing $s$-wave SC is induced by the $3d_{x^2-y^2}$ orbital, driven by the strong interlayer superexchange interaction. To include effects from both $E_g$-orbitals , we establish a two-orbital bilayer $t$-$J$ model. Our calculations reveal that due to the no-double-occupancy constraint, the $3d_{x^2-y^2}$ band and the $3d_{z^2}$ bonding band are flattened by a factor of about 2 and 10, respectively, which is consistent with recent angle-resolved-photo-emission-spectroscopy measurements. Consequently, a high temperature SC can be hardly induced in the $3d_{z^2}$-orbital due to the difficulty to develop phase coherence. However, it can be easily achieved by the $3d_{x^2-y^2}$ orbital under realistic interaction strength. With electron doping, the $3d_{z^2}$-band gradually dives below the Fermi level, but $T_c$ continues to enhance, suggesting that it is not necessary for the high-$T_c$ SC in LNO. With hole doping, $T_c$ initially drops and then rises, accompanied by the crossover from the BCS to BEC-type superconducting transitions.

cond-mat.supr-con↗