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Jing Fan

Jing Fan contributes to research discovery and scholarly infrastructure.

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Published work

11 published item(s)

preprint2026arXiv

Anomalously High Phonon Thermal Conductivity Driven by Weak Electron-Phonon Coupling in Weyl Semimetals TaAs and TaP

In conventional metals, thermal transport is governed by electrons, with phonon contributions often considered negligible. Here, through rigorous first-principles calculations, we uncover a phonon-dominated thermal transport regime in the Weyl semimetals TaAs and TaP. Remarkably, although TaP is metallic, its phonon thermal conductivity ($κ_{\text{ph}}$) reaches as high as 171 Wm$^{-1}$K$^{-1}$ at room temperature, surpassing its electronic counterpart by more than a factor of five. This anomalously high $κ_{\text{ph}}$ is enabled by the unique electronic and phononic band structures, characterized by the Weyl nodes near the Fermi level, together with acoustic phonon bunching and a wide frequency gap in the phonon spectrum, which collectively suppress phonon-electron and phonon-phonon scattering processes. Due to the substantial phonon contribution, the derived Lorenz number deviates strongly from the conventional Wiedemann-Franz law. We further show that the significance of phonon thermal transport is universal across topological semimetals. Our work provides deeper insight into thermal transport mechanisms in topological semimetals and extends the scope for discovering materials with high thermal conductivity.

preprint2026arXiv

SynGR: Unleashing the Potential of Cross-Modal Synergy for Generative Recommendation

Generative Recommendation (GR) has emerged as a promising paradigm by formulating item recommendation as a sequence-to-sequence generation task over item identifiers. Recent studies have incorporated multimodal signals to provide richer token-level evidence for generation. However, existing approaches largely rely on alignment-centric fusion and underexplore synergistic information across modalities. In practice, synergistic information plays a critical role in capturing emergent item properties that cannot be inferred from any single modality alone. Such properties encode intrinsic item semantics and guide user preferences, enabling models to move beyond surface-level feature matching. To address this limitation, we propose \textbf{SynGR}, a synergistic generative recommendation framework that explicitly encourages the exploitation of cross-modal dependencies during generation. By constraining overreliance on dominant modalities, SynGR enables the model to capture emergent item semantics beyond shared or modality-specific signals. Extensive experiments across three benchmark datasets demonstrate that SynGR achieves superior performance.

preprint2024arXiv

CodeFuse-Query: A Data-Centric Static Code Analysis System for Large-Scale Organizations

In the domain of large-scale software development, the demands for dynamic and multifaceted static code analysis exceed the capabilities of traditional tools. To bridge this gap, we present CodeFuse-Query, a system that redefines static code analysis through the fusion of Domain Optimized System Design and Logic Oriented Computation Design. CodeFuse-Query reimagines code analysis as a data computation task, support scanning over 10 billion lines of code daily and more than 300 different tasks. It optimizes resource utilization, prioritizes data reusability, applies incremental code extraction, and introduces tasks types specially for Code Change, underscoring its domain-optimized design. The system's logic-oriented facet employs Datalog, utilizing a unique two-tiered schema, COREF, to convert source code into data facts. Through Godel, a distinctive language, CodeFuse-Query enables formulation of complex tasks as logical expressions, harnessing Datalog's declarative prowess. This paper provides empirical evidence of CodeFuse-Query's transformative approach, demonstrating its robustness, scalability, and efficiency. We also highlight its real-world impact and diverse applications, emphasizing its potential to reshape the landscape of static code analysis in the context of large-scale software development.Furthermore, in the spirit of collaboration and advancing the field, our project is open-sourced and the repository is available for public access

preprint2023arXiv

On the State of German (Abstractive) Text Summarization

With recent advancements in the area of Natural Language Processing, the focus is slowly shifting from a purely English-centric view towards more language-specific solutions, including German. Especially practical for businesses to analyze their growing amount of textual data are text summarization systems, which transform long input documents into compressed and more digestible summary texts. In this work, we assess the particular landscape of German abstractive text summarization and investigate the reasons why practically useful solutions for abstractive text summarization are still absent in industry. Our focus is two-fold, analyzing a) training resources, and b) publicly available summarization systems. We are able to show that popular existing datasets exhibit crucial flaws in their assumptions about the original sources, which frequently leads to detrimental effects on system generalization and evaluation biases. We confirm that for the most popular training dataset, MLSUM, over 50% of the training set is unsuitable for abstractive summarization purposes. Furthermore, available systems frequently fail to compare to simple baselines, and ignore more effective and efficient extractive summarization approaches. We attribute poor evaluation quality to a variety of different factors, which are investigated in more detail in this work: A lack of qualitative (and diverse) gold data considered for training, understudied (and untreated) positional biases in some of the existing datasets, and the lack of easily accessible and streamlined pre-processing strategies or analysis tools. We provide a comprehensive assessment of available models on the cleaned datasets, and find that this can lead to a reduction of more than 20 ROUGE-1 points during evaluation. The code for dataset filtering and reproducing results can be found online at https://github.com/dennlinger/summaries

preprint2022arXiv

Approximation algorithms for covering vertices by long paths

Given a graph, the general problem to cover the maximum number of vertices by a collection of vertex-disjoint long paths seemingly escapes from the literature. A path containing at least $k$ vertices is considered long. When $k \le 3$, the problem is polynomial time solvable; when $k$ is the total number of vertices, the problem reduces to the Hamiltonian path problem, which is NP-complete. For a fixed $k \ge 4$, the problem is NP-hard and the best known approximation algorithm for the weighted set packing problem implies a $k$-approximation algorithm. To the best of our knowledge, there is no approximation algorithm directly designed for the general problem; when $k = 4$, the problem admits a $4$-approximation algorithm which was presented recently. We propose the first $(0.4394 k + O(1))$-approximation algorithm for the general problem and an improved $2$-approximation algorithm when $k = 4$. Both algorithms are based on local improvement, and their theoretical performance analyses are done via amortization and their practical performance is examined through simulation studies.

preprint2022arXiv

Can depth-adaptive BERT perform better on binary classification tasks

In light of the success of transferring language models into NLP tasks, we ask whether the full BERT model is always the best and does it exist a simple but effective method to find the winning ticket in state-of-the-art deep neural networks without complex calculations. We construct a series of BERT-based models with different size and compare their predictions on 8 binary classification tasks. The results show there truly exist smaller sub-networks performing better than the full model. Then we present a further study and propose a simple method to shrink BERT appropriately before fine-tuning. Some extended experiments indicate that our method could save time and storage overhead extraordinarily with little even no accuracy loss.

preprint2022arXiv

IC-GVINS: A Robust, Real-time, INS-Centric GNSS-Visual-Inertial Navigation System for Wheeled Robot

In this letter, we present a robust, real-time, inertial navigation system (INS)-Centric GNSS-Visual-Inertial navigation system (IC-GVINS) for wheeled robot, in which the precise INS is fully utilized in both the state estimation and visual process. To improve the system robustness, the INS information is employed during the whole keyframe-based visual process, with strict outlier-culling strategy. GNSS is adopted to perform an accurate and convenient initialization of the IC-GVINS, and is further employed to achieve absolute positioning in large-scale environments. The IMU, visual, and GNSS measurements are tightly fused within the framework of factor graph optimization. Dedicated experiments were conducted to evaluate the robustness and accuracy of the IC-GVINS on a wheeled robot. The IC-GVINS demonstrates superior robustness in various visual-degenerated scenes with moving objects. Compared to the state-of-the-art visual-inertial navigation systems, the proposed method yields improved robustness and accuracy in various environments. We open source our codes combined with the dataset on GitHub

preprint2021arXiv

Electric and magnetic fields tuned spin-polarized topological phases in two-dimensional ferromagnetic MnBi$_4$Te$_7$

Applying electric or magnetic fields is widely used to not only create and manipulate topological states but also facilitate their observations in experiments. In this work, we show by first-principles calculations and topological analysis that the time-reversal (TR) symmetry-broken quantum spin Hall (QSH) state emerges in a two-dimensional ferromagnetic MnBi$_4$Te$_7$ monolayer. This TR-symmetry broken QSH phase possesses a highly tunable nontrivial band gap under an external electric field (or tuning interlayer distance). Furthermore, based on the Wannier-function-based tight-binding approach, we reveal that a topological phase transition from the TR-symmetry broken QSH phase to the quantum anomalous Hall (QAH) phase occurs with the increase of magnetic fields. Besides, we also find that a reverse electric fields can facilitate the realization of QAH phase. Our work not only uncovers the ferromagnetic topological properties the MnBi$_4$Te$_7$ monolayer tuned by electric and magnetic fields, but also can stimulate further applications to spintronics and topological devices.

preprint2021arXiv

Floquet Valley-Polarized Quantum Anomalous Hall State in Nonmagnetic Heterobilayers

The valley-polarized quantum anomalous Hall (VQAH) state, which forwards a strategy for combining valleytronics and spintronics with nontrivial topology, attracts intensive interest in condensed-matter physics. So far, the explored VQAH states have still been limited to magnetic systems. Here, using the low-energy effective model and Floquet theorem, we propose a different mechanism to realize the Floquet VQAH state in nonmagnetic heterobilayers under light irradiation. We then realize this proposal via first-principles calculations in transition metal dichalcogenide heterobilayers, which initially possess the time-reversal invariant valley quantum spin Hall (VQSH) state. By irradiating circularly polarized light, the time-reversal invariant VQSH state can evolve into the VQAH state, behaving as an optically switchable topological spin-valley filter. These findings not only offer a rational scheme to realize the VQAH state without magnetic orders, but also pave a fascinating path for designing topological spintronic and valleytronic devices.

preprint2021arXiv

Magnetic field induced Valley-Polarized Quantum Anomalous Hall Effects in Ferromagnetic van der Waals Heterostructures

The valley-polarized quantum anomalous Hall effect (VQAHE) attracts intensive interest since it uniquely combines valleytronics and spintronics with nontrivial band topology. Here, based on first-principles calculations and Wannier-function-based tight-binding (WFTB) model, we reveal that valley-based Hall effects and especially the VQAHE induced by external magnetic fields can occur in two-dimensional (2D) ferromagnetic van der Waals heterostructures (vdWHs). The results show that considerable valley-splitting derived from the Zeeman exchange energy drives these vdWHs generating the valley anomalous Hall effect and then the VQAHE. The chiral edge states and quantized Hall conductance are utilized to confirm the presence of VQAHE. Besides, it is also found that external electric fields (or tuning interlayer distances) can facilitate the realization of VQAHE, and thus we present a phase diagram in a broad parameter regime of magnetic fields and electric fields (or interlayer distances). Our work not only offers a class of ferromagnetic vdWHs to realize various valley-based Hall phases, but also can guide advancements for designing topological devices with spin-valley filtering effects based on the VQAHE.

preprint2020arXiv

Intrinsic quantum anomalous Hall phase induced by proximity in germanene/Cr$_2$Ge$_2$Te$_6$ van der Waals heterostructure

A van der Waals heterostructure combined with intrinsic magnetism and topological orders have recently paved attractive avenues to realize quantum anomalous Hall effects. In this work, using first-principles calculations and effective model analysis, we propose that the robust quantum anomalous Hall states with sizable band gaps emerge in the van der Waals heterostructure of germanene/Cr$_2$Ge$_2$Te$_6$. This heterostructure possesses high thermodynamic stability, thus facilitating its experimental fabrication. Furthermore, we uncover that the proximity effect enhances the coupling between the germanene and Cr$_2$Ge$_2$Te$_6$ layers, inducing the nontrivial band gaps in a wide range from 29 meV to 72 meV. The chiral edge states inside the band gap, leading to Hall conductance quantized to $-e^2/h$, are clearly visible. This findings provide an ideal candidate to detect the quantum anomalous Hall states and realize further applications to nontrivial quantum transport at a high temperature.