arXiv subjects

Yi Lu

Publications and source records attributed to Yi Lu.

At least 109 records · Page 6Linked to original sources

arXiv2025

Lee Yang edge singularities of QCD in association with Roberge-Weiss phase transition and chiral phase transition

We study the Quantum Chromodynamics (QCD) phase transitions in the complex chemical potential plane in the framework of Dyson-Schwinger equation approach, in the presence of a constant gluonic background field that represents confining dynamics. We solve the quark gap equation and the background field equation self consistently, which allows us to directly explore the confinement phase transition and furthermore, evaluate the impact of the back-coupling of confinement on chiral symmetry breaking. Moreover, within such a coupled framework towards the complex chemical potential region, we demonstrate the emergence of Roberge-Weiss (RW) symmetry and investigate the trajectory of Lee-Yang edge singularities (LYES). Our analysis reveals that the LYES scaling behavior is similar to our previous findings without the background field condensate. However, a significant difference from our earlier work is that the trajectory of LYES terminates when the imaginary part of the singularity becomes $1/3 \, πT$. We elaborate that this cut-off behavior is caused by the RW symmetry that is symmetric to the imaginary chemical potential $μ_i=1/3 \, πT$.

hep-ph↗

arXiv2025

Finite density signatures of confining and chiral dynamics in QCD thermodynamics and fluctuations of conserved charges

We evaluate thermodynamic observables such as pressure, baryon number, entropy and energy density, as well as the second and fourth order baryon number cumulants in the phase structure of QCD. The intertwined confinement and chiral dynamics is resolved within functional QCD, aiming for quantitative accuracy at larger densities. Specifically it is shown that the self-consistent resolution of the confining gluonic background is crucial in particular for even the qualitative properties of the cumulants. Our results are in quantitative agreement with lattice and functional QCD benchmarks at vanishing and small chemical potentials. Moreover, they offer novel insights in the dynamics at larger chemical potentials including the regime of the critical end point. A welcome by-product of this analysis is the computation of the Polyakov loop potential in finite density QCD, which, alongside the aforementioned observables, can be used as input and benchmark for effective theory computations at finite density.

hep-ph↗

arXiv2025

ScholarCopilot: Training Large Language Models for Academic Writing with Accurate Citations

Academic writing requires both coherent text generation and precise citation of relevant literature. Although recent Retrieval-Augmented Generation (RAG) systems have significantly improved factual accuracy in general-purpose text generation, their ability to support professional academic writing remains limited. In this work, we introduce ScholarCopilot, a unified framework designed to enhance existing large language models for generating professional academic articles with accurate and contextually relevant citations. ScholarCopilot dynamically determines when to retrieve scholarly references by generating a retrieval token [RET], which is then used to query a citation database. The retrieved references are fed into the model to augment the generation process. We jointly optimize both the generation and citation tasks within a single framework to improve efficiency. Our model is built upon Qwen-2.5-7B and trained on 500K papers from arXiv. It achieves a top-1 retrieval accuracy of 40.1% on our evaluation dataset, outperforming baselines such as E5-Mistral-7B-Instruct (15.0%) and BM25 (9.8%). On a dataset of 1,000 academic writing samples, ScholarCopilot scores 16.2/25 in generation quality -- measured across relevance, coherence, academic rigor, completeness, and innovation -- significantly surpassing all existing models, including much larger ones like the Retrieval-Augmented Qwen2.5-72B-Instruct. Human studies further demonstrate that ScholarCopilot, despite being a 7B model, significantly outperforms ChatGPT, achieving 100% preference in citation quality and over 70% in overall usefulness.

cs.CL↗

arXiv2025

Illustrating the liquid gas transition of nuclear matter in QCD

We demonstrate that the liquid-gas transition of nuclear matter can be rigorously described with the quantum chromodynamics by combining the quark gap equation and the Faddeev equation of nucleon. Our investigation focuses on this transition at zero temperature and finite chemical potential, revealing a finite difference between the gas and liquid solution of the quark propagator. This difference emerges from the shift of the nucleon pole mass in medium, which is generated in the nucleon channel of the quark gap equation. We prove that such a difference is precisely the contour contribution from the shift of the nucleon pole. The resulting discontinuity manifests as a first-order phase transition and fundamentally determines both the nuclear binding energy and the saturation density. We then derive an analytical relation between the binding energy and the sigma term of the nucleon, yielding a binding energy of $E/A=15.9\,\textrm{MeV}$. Furthermore, by establishing the relation between the nuclear saturation density and the vector charge of nucleon in association with the binding energy, we determine the saturation density to be $n_{\textrm{B}}^{0}=0.15\,\textrm{fm}^{-3}$.

nucl-th↗

arXiv2025

Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations

Recently, multimodal large language models (MLLMs) have demonstrated remarkable performance in visual-language tasks. However, the authenticity of the responses generated by MLLMs is often compromised by object hallucinations. We identify that a key cause of these hallucinations is the model's over-susceptibility to specific image frequency features in detecting objects. In this paper, we introduce Multi-Frequency Perturbations (MFP), a simple, cost-effective, and pluggable method that leverages both low-frequency and high-frequency features of images to perturb visual feature representations and explicitly suppress redundant frequency-domain features during inference, thereby mitigating hallucinations. Experimental results demonstrate that our method significantly mitigates object hallucinations across various model architectures. Furthermore, as a training-time method, MFP can be combined with inference-time methods to achieve state-of-the-art performance on the CHAIR benchmark.

cs.CV↗

arXiv2025

Interlayer Hopping between Surface Mott Insulator and Bulk Band Insulator in layered 1T-TaS_{2}

In condensed matter physics, various mechanisms give rise to distinct insulating phases. The competition and interplay between these phases remain elusive, even for the seemingly most distinguishable band and Mott insulators. In multilayer systems, such interplay is mediated by interlayer hopping, which competes with the Coulomb repulsion to determine the nature of insulators. The layered compound 1T-TaS_{2} provides an ideal platform for investigating this phenomenon, as it naturally hosts coexisting Mott and band insulating states. However, distinguishing these distinct insulating states and characterizing the evolution remain challenging. In this study, we employ a dual approach utilizing surface-sensitive High-Resolution Electron Energy Loss Spectroscopy (HREELS) and bulk-sensitive Fourier-transform Infrared Spectroscopy (FTIR) to investigate the electronic excitation spectrum of 1T-TaS_{2}. Our methodology effectively identifies the features originating from the Mott and band insulators by analyzing the differences in their bulk and surface spectral weights, along with their energy distinctions. Based on the previous identification, we further investigate the evolution of insulating state features in the homostructure as they are modulated by temperature. The measurements and Dynamical Mean-Field Theory (DMFT) calculations suggest that the softening and broadening of Hubbard excitations in the Mott state with increasing temperature result from enhanced interlayer hopping between the Mott and band insulators.

cond-mat.mtrl-sci↗

arXiv2025

Enhancing The Open Network: Definition and Automated Detection of Smart Contract Defects

The Open Network (TON), designed to support Telegram's extensive user base of hundreds of millions, has garnered considerable attention since its launch in 2022. FunC is the most popular programming language for writing smart contracts on TON. It is distinguished by a unique syntax compared to other smart contract languages. Despite growing interest, research on the practical defects of TON smart contracts is still in its early stages. In this paper, we summarize eight smart contract defects identified from TON's official blogs and audit reports, each with detailed definitions and code examples. Furthermore, we propose a static analysis framework called TONScanner to facilitate the detection of these defects. Specifically, TONScanner reuses FunC compiler's frontend code to transform the FunC source code into FunC intermediate representation (IR) in the form of a directed acyclic graph (DAG). Based on this IR, TONScanner constructs a control flow graph (CFG), then transforms it into a static single assignment (SSA) form to simplify further analysis. TONScanner also integrates Data Dependency, Call Graph, Taint Analysis, and Cell Construct, which are specifically tailored for TON blockchain's unique data structures. These components finally facilitate the identification of the eight defects. We evaluate the effectiveness of TONScanner by applying it to 1,640 smart contracts and find a total of 14,995 defects. Through random sampling and manual labeling, we find that TONScanner achieves an overall precision of 97.49%. The results reveal that current TON contracts contain numerous defects, indicating that developers are prone to making errors. TONScanner has proven its ability to accurately identify these defects, thereby aiding in their correction.

cs.SE↗

arXiv2024

The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio

With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for generalized detection methods. ALM-based deepfake audio currently exhibits widespread, high deception, and type versatility, posing a significant challenge to current audio deepfake detection (ADD) models trained solely on vocoded data. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially constructed the Codecfake dataset, an open-source, large-scale collection comprising over 1 million audio samples in both English and Chinese, focus on ALM-based audio detection. As countermeasure, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original sharpness aware minimization (SAM), we propose the CSAM strategy to learn a domain balanced and generalized minima. In our experiments, we first demonstrate that ADD model training with the Codecfake dataset can effectively detects ALM-based audio. Furthermore, our proposed generalization countermeasure yields the lowest average equal error rate (EER) of 0.616% across all test conditions compared to baseline models. The dataset and associated code are available online.

cs.SD↗

arXiv2024

Low-temperature mean valence of nickel ions in pressurized La$_3$Ni$_2$O$_7$

The discovery of high critical temperature (Tc) superconductivity in pressurized La$_3$Ni$_2$O$_7$ has ignited renewed excitement in the search of novel high-Tc superconducting compounds with 3d transition metals. Compared to other ambient-pressure superconductors, such as copper-oxide and iron-oxypnictides, unraveling the mechanisms of the pressure-induced superconductivity poses significant and unique challenges. A critical factor in this phenomenon seems to be related to the electronic configuration of 3d orbitals, which may play a fundamental role in driving high-Tc superconductivity. However, the pressure effects on the mixed-valence states of 3d-orbital cations and their influence on the emergence of high-Tc superconductivity remain poorly understood. Here, we use high-pressure (P) and low-temperature synchrotron X-ray absorption spectroscopy to investigate the influence of pressure on the mean valence change of Ni ions in La$_3$Ni$_2$O$_7$. Our results demonstrate that at a low-temperature of 20 K, the mean valence remains relatively stable across the pressures range from 1 atm to 40 GPa. Based on analyzing the absorption data, we find that, at a critical pressure, the ambient-pressure ordered phases disappear and both the structural and the superconducting phase transition occur. The pressure-induced structural phase transition revealed by our absorption results is consistent with that determined by X-ray diffraction, offering new information for a comprehensive understanding on the pressure-induced superconductivity in La$_3$Ni$_2$O$_7$.

cond-mat.supr-con↗

arXiv2024

Video Repurposing from User Generated Content: A Large-scale Dataset and Benchmark

The demand for producing short-form videos for sharing on social media platforms has experienced significant growth in recent times. Despite notable advancements in the fields of video summarization and highlight detection, which can create partially usable short films from raw videos, these approaches are often domain-specific and require an in-depth understanding of real-world video content. To tackle this predicament, we propose Repurpose-10K, an extensive dataset comprising over 10,000 videos with more than 120,000 annotated clips aimed at resolving the video long-to-short task. Recognizing the inherent constraints posed by untrained human annotators, which can result in inaccurate annotations for repurposed videos, we propose a two-stage solution to obtain annotations from real-world user-generated content. Furthermore, we offer a baseline model to address this challenging task by integrating audio, visual, and caption aspects through a cross-modal fusion and alignment framework. We aspire for our work to ignite groundbreaking research in the lesser-explored realms of video repurposing.

cs.CV↗

arXiv2024

Interpreting and Improving Attention From the Perspective of Large Kernel Convolution

Attention mechanisms have significantly advanced visual models by capturing global context effectively. However, their reliance on large-scale datasets and substantial computational resources poses challenges in data-scarce and resource-constrained scenarios. Moreover, traditional self-attention mechanisms lack inherent spatial inductive biases, making them suboptimal for modeling local features critical to tasks involving smaller datasets. In this work, we introduce Large Kernel Convolutional Attention (LKCA), a novel formulation that reinterprets attention operations as a single large-kernel convolution. This design unifies the strengths of convolutional architectures locality and translation invariance with the global context modeling capabilities of self-attention. By embedding these properties into a computationally efficient framework, LKCA addresses key limitations of traditional attention mechanisms. The proposed LKCA achieves competitive performance across various visual tasks, particularly in data-constrained settings. Experimental results on CIFAR-10, CIFAR-100, SVHN, and Tiny-ImageNet demonstrate its ability to excel in image classification, outperforming conventional attention mechanisms and vision transformers in compact model settings. These findings highlight the effectiveness of LKCA in bridging local and global feature modeling, offering a practical and robust solution for real-world applications with limited data and resources.

cs.CV↗

arXiv2024

Evolution of Electronic Correlations in the Ruddlesden-Popper Nickelates

We report on optical studies of the Ruddlesden-Popper nickelates La$_{n+1}$Ni$_{n}$O$_{3n+1}$ with $n = 2$ (La$_{3}$Ni$_{2}$O$_{7}$), $n = 3$ (La$_{4}$Ni$_{3}$O$_{10}$) and $n = \infty$ (LaNiO$_{3}$). As the number of the NiO$_{6}$ octahedra layers $n$ grows, the ratio of the kinetic energy determined from the experimental optical conductivity and that from band theory $K_{\text{exp}}/K_{\text{band}}$ increases, suggesting a reduction of electronic correlations. While the strong electronic correlations in the bilayer La$_{3}$Ni$_{2}$O$_{7}$ place it on the verge of the Mott insulating phase, the trilayer La$_{4}$Ni$_{3}$O$_{10}$ and infinite-layer LaNiO$_{3}$ exhibit moderate electronic correlations, falling into the regime of correlated metals. The evolution of the electronic correlations in La$_{n+1}$Ni$_{n}$O$_{3n+1}$ is likely to be dominated by the Ni-$d_{z^2}$ orbital. Our results provide important information for understanding the superconductivity in Ruddlesden-Popper nickelates.

cond-mat.supr-con↗

arXiv2024

APDDv2: Aesthetics of Paintings and Drawings Dataset with Artist Labeled Scores and Comments

Datasets play a pivotal role in training visual models, facilitating the development of abstract understandings of visual features through diverse image samples and multidimensional attributes. However, in the realm of aesthetic evaluation of artistic images, datasets remain relatively scarce. Existing painting datasets are often characterized by limited scoring dimensions and insufficient annotations, thereby constraining the advancement and application of automatic aesthetic evaluation methods in the domain of painting. To bridge this gap, we introduce the Aesthetics Paintings and Drawings Dataset (APDD), the first comprehensive collection of paintings encompassing 24 distinct artistic categories and 10 aesthetic attributes. Building upon the initial release of APDDv1, our ongoing research has identified opportunities for enhancement in data scale and annotation precision. Consequently, APDDv2 boasts an expanded image corpus and improved annotation quality, featuring detailed language comments to better cater to the needs of both researchers and practitioners seeking high-quality painting datasets. Furthermore, we present an updated version of the Art Assessment Network for Specific Painting Styles, denoted as ArtCLIP. Experimental validation demonstrates the superior performance of this revised model in the realm of aesthetic evaluation, surpassing its predecessor in accuracy and efficacy. The dataset and model are available at https://github.com/BestiVictory/APDDv2.git.

cs.CV↗

arXiv2024

Wafer-scale Semiconductor Grafting: Enabling High-Performance, Lattice-Mismatched Heterojunctions

Semiconductor heterojunctions are foundational to many advanced electronic and optoelectronic devices. However, achieving high-quality, lattice-mismatched interfaces remains challenging, limiting both scalability and device performance. Semiconductor grafting offers a promising solution by directly forming electrically active, lattice-mismatched heterojunctions between dissimilar materials. However, its scalability and uniformity at the wafer level have yet to be demonstrated. This work demonstrates the achievement of highly uniform, reproducible results across silicon, sapphire, and gallium nitride (GaN) substrates using wafer-scale semiconductor grafting. To illustrate this scalability, we conducted an in-depth study of a grafted Si/GaN heterojunction, examining band alignment through X-ray photoelectron spectroscopy and confirming crystallinity and interfacial integrity with scanning transmission electron microscopy. The resulting p-n diodes exhibit significantly enhanced electrical performance and wafer-scale uniformity compared to conventional approaches. This work establishes wafer-scale semiconductor grafting as a versatile and scalable technology, bridging the gap between laboratory-scale research and industrial manufacturing for heterogeneous semiconductor integration, and paving the way for novel, high-performance electronic and optoelectronic devices.

physics.app-ph↗

arXiv2024

Shannon entropy of optimized proton-neutron pair condensates

Proton-neutron pairing and like-nucleon pairing are two different facets of atomic nuclear configurations. While like-nucleon pair condensates manifest their superfluidic nature in semi magic nuclei, it is not absolutely clear if there exists a T=0 proton-neutron pair condensate phase in $N=Z$ nuclei. With an explicit formalism of general pair condensates with good particle numbers, we optimize proton-neutron pair condensates for all $N=Z$ nuclei between $^{16}$O and $^{100}$Sn, given shell model effective interactions. As comparison, we also optimize like-nucleon pair condensates for their semi-magic isotones. Shannon entanglement entropy is a measurement of mixing among pair configurations, and can signal intrinsic phase transition. It turns out the like-nucleon pair condensates for semi-magic nuclei have large entropies signaling an entangled phase, but the proton-neutron pair condensates end up not far from a Hartree-Fock solution, with small entropy. With artificial pairing interaction strengths, we show that the general proton-neutron pair condensate can transit from an entangled T=1 phase to an entangled T=0 phase, i.e. pairing phase transition driven by external parameters. In the T=0 limit, the proton-neutron pair condensate optimized for $^{24}$Mg turns out to be a purely P pair condensate with large entanglement entropy, although such cases may occur in cold atom systems, unlikely in atomic nuclei.

nucl-th↗

arXiv2024

Si/AlN p-n heterojunction interfaced with ultrathin SiO2

Ultra-wide bandgap (UWBG) materials hold immense potential for high-power RF electronics and deep ultraviolet photonics. Among these, AlGaN emerges as a promising candidate, offering a tunable bandgap from 3.4 eV (GaN) to 6.1 eV (AlN) and remarkable material characteristics. However, achieving efficient p-type doping in high aluminum composition AlGaN remains a formidable challenge. This study presents an alternative approach to address this issue by fabricating a p+ Si/n-AlN/n+ AlGaN heterojunction structure by following the semiconductor grafting technique. Atomic force microscopy (AFM) analysis revealed that the AlN and the nanomembrane surface exhibited a smooth topography with a roughness of 1.96 nm and 0.545 nm, respectively. High-angle annular dark field scanning transmission electron microscopy (HAADF-STEM) confirmed a sharp and well-defined Si/AlN interface, with minimal defects and strong chemical bonding, crucial for efficient carrier transport. X-ray photoelectron spectroscopy (XPS) measurements demonstrated a type-I heterojunction with a valence band offset of 2.73 eV-2.84 eV and a conduction band offset of 2.22 eV -2.11 eV. The pn diode devices exhibited a linear current-voltage (I-V) characteristic, an ideality factor of 1.92, and a rectification ratio of 3.3E4, with a turn-on voltage of indicating effective p-n heterojunction. Temperature-dependent I-V measurements showed stable operation up to 90 C. The heterojunction's high-quality interface and electrical performance showcase its potential for advanced AlGaN-based optoelectronic and electronic devices.

physics.app-ph↗

arXiv2024

Single-crystalline GaAs/Si Heterojunction Tunnel Diodes Interfaced by an Ultrathin Oxygen-enriched Layer

We report the fabrication and characteristics of GaAs/Si p+/n+ heterojunction tunnel diodes. These diodes were fabricated via grafting the freestanding single-crystalline p-type degenerately doped GaAs (4E19 cm-3) nanomembrane (NM) onto single-crystalline n-type Si (5E19 cm-3) substrate. At the heterointerface, an amorphous ultrathin oxygen-enriched layer (UOL) was intentionally engineered through chemical oxidation and atomic layer deposition (ALD). Scanning transmission electron microscopy (STEM) confirmed the formation of the UOL and the single crystallinity of the grafted junction. The resulting tunnel diodes consistently exhibited negative differential resistance (NDR) behavior at room temperature, with a high maximum peak-to-valley current ratio (PVCR) of 36.38, valley voltages ranging from 1.3 to 1.8 V, and a peak tunneling current density of 0.95 kA/cm2. This study not only highlights the critical roles of the UOL as both an interface improvement layer and a quantum tunneling medium, but also establishes "semiconductor grafting" as an effective and versatile method for high-performance, lattice-mismatched heterojunction devices.

physics.app-ph↗

arXiv2024

A Controlled Study on Long Context Extension and Generalization in LLMs

Broad textual understanding and in-context learning require language models that utilize full document contexts. Due to the implementation challenges associated with directly training long-context models, many methods have been proposed for extending models to handle long contexts. However, owing to differences in data and model classes, it has been challenging to compare these approaches, leading to uncertainty as to how to evaluate long-context performance and whether it differs from standard evaluation. We implement a controlled protocol for extension methods with a standardized evaluation, utilizing consistent base models and extension data. Our study yields several insights into long-context behavior. First, we reaffirm the critical role of perplexity as a general-purpose performance indicator even in longer-context tasks. Second, we find that current approximate attention methods systematically underperform across long-context tasks. Finally, we confirm that exact fine-tuning based methods are generally effective within the range of their extension, whereas extrapolation remains challenging. All codebases, models, and checkpoints will be made available open-source, promoting transparency and facilitating further research in this critical area of AI development.

cs.CL↗