arXiv subjects

Li Yang

Publications and source records attributed to Li Yang.

At least 109 records · Page 6Linked to original sources

arXiv2024

A new pathway to impact ionization in a photo-excited one-dimensional ionic Hubbard model

Using the time-dependent Lanczos method, we study the non-equilibrium dynamics of the half-filled one-dimensional ionic Hubbard model, deep within the Mott insulating regime, under the influence of a transient laser pulse. In equilibrium, increasing the staggered potential in the Mott regime reduces the Mott gap and broadens the Hubbard bands, creating favorable conditions for impact ionization. After laser excitation, impact ionization is observed, with its occurrence depending on both the staggered potential and the laser pump frequency. By analyzing the time evolution of the kinetic, ionic, and Coulomb interaction energies, we identify a novel mechanism for impact ionization, in which excess ionic potential energy is converted into additional double occupancy-distinct from the conventional mechanism where excess kinetic energy drives this process. We further show that impact ionization arises from interference between excited states driven by photon excitation of the same order. These results present a new pathway for realizing impact ionization in strongly correlated electron systems.

cond-mat.str-el↗

arXiv2024

Lattice Vibration, Raman Modes and Room-Temperature Spin-Phonon Coupling in Intrinsic 2D van der Waals Ferromagnetic Fe3GaTe2

Two-dimensional (2D) van der Waals (vdW) magnets with spin-phonon coupling are crucial for next-generation spintronics. Among them, Fe3GaTe2 has attracted widespread attention due to above-room-temperature intrinsic ferromagnetism and large perpendicular magnetic anisotropy. However, the lattice vibrations and the interplay between ferromagnetism and lattice vibrations in Fe3GaTe2 are still unexplored. Here, we report the lattice vibration, Raman modes, and room-temperature spin-phonon coupling in 2D Fe3GaTe2 with above-room-temperature Curie temperature (TC). Two typical Raman modes with out-of-plane lattice vibrations are identified: "A" _"1g" ^"1" and "A" _"1g" ^"2" , whose frequencies increase as the thickness decreases from bulk to 2D Fe3GaTe2 due to the weakening of interlayer vdW interactions and spin exchange coupling. Moreover, the difference between phonon band dispersions under ferromagnetic and nonmagnetic interlayer spin ordering indicates the existence of spin-phonon coupling. The phonon frequency diverges from the anharmonic model below TC and thus the strength of spin-phonon coupling is ~0.28 cm-1 at 300 K, which is the first experimental identification of room-temperature spin-phonon coupling in 2D vdW magnets. This work deepens the understanding of novel 2D vdW magnets and provides a basis for spintronic applications at and above room temperature.

cond-mat.mtrl-sci↗

arXiv2024

Twisted Type-II Rashba Homobilayer: A Platform for Tunable Topological Flat Bands

The recent discovery of topological flat bands in twisted transition metal dichalcogenide homobilayers and multilayer graphene has sparked significant research interest. We propose a new platform for realizing tunable topological moire flat bands: twisted type-II Rashba homobilayers. The interplay between Rashba spin-orbit coupling and interlayer interactions generates an effective pseudo-antiferromagnetic field, opening a gap within the Dirac cone with non-zero Berry curvature. Using twisted BiTeI bilayers as an example, we predict the emergence of flat topological bands with a remarkably narrow bandwidth (below 20 meV). Notably, the system undergoes a transition from a valley Hall insulator to a quantum spin Hall insulator as the twisting angle increases. This transition arises from a competition between the twisting-driven effective spin-orbit coupling and sublattice onsite energies presented in type-II Rashba moiré structures. The high tunability of Rashba materials in terms of the spin-orbit coupling strength, interlayer interaction, and twisting angle expands the range of materials suitable for realizing and manipulating correlated topological properties.

cond-mat.mtrl-sci↗

arXiv2024

UOTe: Kondo-interacting topological antiferromagnet in a van der Waals lattice

Since the initial discovery of two-dimensional van der Waals (vdW) materials, significant effort has been made to incorporate the three properties of magnetism, band structure topology, and strong electron correlations $-$ to leverage emergent quantum phenomena and expand their potential applications. However, the discovery of a single vdW material that intrinsically hosts all three ingredients has remained an outstanding challenge. Here we report the discovery of a Kondo-interacting topological antiferromagnet in the vdW 5$f$ electron system UOTe. It has a high antiferromagnetic (AFM) transition temperature of 150 K, with a unique AFM configuration that breaks the combined parity and time reversal ($PT$) symmetry in an even number of layers while maintaining zero net magnetic moment. Our angle-resolved photoemission spectroscopy (ARPES) measurements reveal Dirac bands near the Fermi level, which combined with our theoretical calculations demonstrate UOTe as an AFM Dirac semimetal. Within the AFM order, we observed the presence of the Kondo interaction, as evidenced by the emergence of a 5$f$ flat band near the Fermi level below 100 K and hybridization between the Kondo band and the Dirac band. Our density functional theory calculations in its bilayer form predict UOTe as a rare example of a fully-compensated AFM Chern insulator.

cond-mat.str-el↗

arXiv2024

Smart-LLaMA: Two-Stage Post-Training of Large Language Models for Smart Contract Vulnerability Detection and Explanation

With the rapid development of blockchain technology, smart contract security has become a critical challenge. Existing smart contract vulnerability detection methods face three main issues: (1) Insufficient quality of datasets, lacking detailed explanations and precise vulnerability locations. (2) Limited adaptability of large language models (LLMs) to the smart contract domain, as most LLMs are pre-trained on general text data but minimal smart contract-specific data. (3) Lack of high-quality explanations for detected vulnerabilities, as existing methods focus solely on detection without clear explanations. These limitations hinder detection performance and make it harder for developers to understand and fix vulnerabilities quickly, potentially leading to severe financial losses. To address these problems, we propose Smart-LLaMA, an advanced detection method based on the LLaMA language model. First, we construct a comprehensive dataset covering four vulnerability types with labels, detailed explanations, and precise vulnerability locations. Second, we introduce Smart Contract-Specific Continual Pre-Training, using raw smart contract data to enable the LLM to learn smart contract syntax and semantics, enhancing their domain adaptability. Furthermore, we propose Explanation-Guided Fine-Tuning, which fine-tunes the LLM using paired vulnerable code and explanations, enabling both vulnerability detection and reasoned explanations. We evaluate explanation quality through LLM and human evaluation, focusing on Correctness, Completeness, and Conciseness. Experimental results show that Smart-LLaMA outperforms state-of-the-art baselines, with average improvements of 6.49% in F1 score and 3.78% in accuracy, while providing reliable explanations.

cs.CR↗

arXiv2024

NFT1000: A Cross-Modal Dataset for Non-Fungible Token Retrieval

With the rise of "Metaverse" and "Web 3.0", Non-Fungible Token (NFT) has emerged as a kind of pivotal digital asset, garnering significant attention. By the end of March 2024, more than 1.7 billion NFTs have been minted across various blockchain platforms. To effectively locate a desired NFT, conducting searches within a vast array of NFTs is essential. The challenge in NFT retrieval is heightened due to the high degree of similarity among different NFTs, regarding regional and semantic aspects. In this paper, we will introduce a benchmark dataset named "NFT Top1000 Visual-Text Dataset" (NFT1000), containing 7.56 million image-text pairs, and being collected from 1000 most famous PFP1 NFT collections2 by sales volume on the Ethereum blockchain. Based on this dataset and leveraging the CLIP series of pre-trained models as our foundation, we propose the dynamic masking fine-tuning scheme. This innovative approach results in a 7.4\% improvement in the top1 accuracy rate, while utilizing merely 13\% of the total training data (0.79 million vs. 6.1 million). We also propose a robust metric Comprehensive Variance Index (CVI) to assess the similarity and retrieval difficulty of visual-text pairs data. The dataset will be released as an open-source resource. For more details, please refer to: https://github.com/ShuxunoO/NFT-Net.git.

cs.IR↗

arXiv2024

Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies

Evaluation of policies in recommender systems typically involves A/B testing using live experiments on real users to assess a new policy's impact on relevant metrics. This ``gold standard'' comes at a high cost, however, in terms of cycle time, user cost, and potential user retention. In developing policies for ``onboarding'' new users, these costs can be especially problematic, since on-boarding occurs only once. In this work, we describe a simulation methodology used to augment (and reduce) the use of live experiments. We illustrate its deployment for the evaluation of ``preference elicitation'' algorithms used to onboard new users of the YouTube Music platform. By developing counterfactually robust user behavior models, and a simulation service that couples such models with production infrastructure, we are able to test new algorithms in a way that reliably predicts their performance on key metrics when deployed live. We describe our domain, our simulation models and platform, results of experiments and deployment, and suggest future steps needed to further realistic simulation as a powerful complement to live experiments.

cs.IR↗

arXiv2024

Manipulating Photogalvanic Effects in Two-Dimensional Multiferroic Breathing Kagome Materials

Multiferroic materials, known for their multiple tunable orders, present an exceptional opportunity to manipulate nonlinear optical responses, which are sensitive to symmetry. In this study, we propose leveraging electric and magnetic fields to selectively control and switch specific types of photogalvanic effects in two-dimensional multiferroic breathing kagome materials. Taking monolayer Nb3I8 as an example, we demonstrate that the shift current, characterized by the real-space shift of electrons and holes, is predominantly unaffected by magnetic order. In contrast, injection current, featured by quantum metric dipole in momentum space, is closely related to valley polarization which can be controlled by magnetic field. Furthermore, both photocurrents can be reversed by out-of-plane electric field via the lattice breathing. Our findings reveal the potential of multiferroic beathing kagome structures for multifunctional optoelectronic applications and sensors.

cond-mat.mes-hall↗

arXiv2024

Enhanced magnetoelectric coupling in two-dimensional hybrid multiferroic heterostructures

Magnetoelectric coupling in insulating multiferroic materials is invaluable for both fundamental research and multifunctional device applications. However, material realization remains a significant challenge. We employ first-principles calculations to predict an enhanced and stacking-robust magnetoelectric coupling in a multiferroic van der Waals heterostructure consisting of CrCl3 and CuCrP2S6. Within this heterostructure, the intralayer ferroic orders exhibit characteristics of a type-I multiferroic, while the interlayer ferroic orders resemble those of a type-II multiferroic, thus creating a hybrid-type multiferroic. Notably, the interlayer magnetic configuration can be switched upon electric polarization reversal, while maintaining the insulating property without any nontrivial phase transitions. Our analysis reveals a strongly interfacial spin local-field effect and proximity polarization effect on band alignments. These factors work synergistically to decide the interlayer exchange interaction and drive enhanced magnetoelectric coupling. Finally, we show that this magnetoelectric effect is robust in multi-layer structures, exhibiting a dependence on the layer number's parity. This research offers promising prospects for discovering enhanced magnetoelectric couplings in hybrid multiferroic van der Waals heterostructures.

cond-mat.mtrl-sci↗

arXiv2024

AIM 2024 Challenge on Video Saliency Prediction: Methods and Results

This paper reviews the Challenge on Video Saliency Prediction at AIM 2024. The goal of the participants was to develop a method for predicting accurate saliency maps for the provided set of video sequences. Saliency maps are widely exploited in various applications, including video compression, quality assessment, visual perception studies, the advertising industry, etc. For this competition, a previously unused large-scale audio-visual mouse saliency (AViMoS) dataset of 1500 videos with more than 70 observers per video was collected using crowdsourced mouse tracking. The dataset collection methodology has been validated using conventional eye-tracking data and has shown high consistency. Over 30 teams registered in the challenge, and there are 7 teams that submitted the results in the final phase. The final phase solutions were tested and ranked by commonly used quality metrics on a private test subset. The results of this evaluation and the descriptions of the solutions are presented in this report. All data, including the private test subset, is made publicly available on the challenge homepage - https://challenges.videoprocessing.ai/challenges/video-saliency-prediction.html.

cs.CV↗

arXiv2024

Insulator to Metal Transition under High Pressure in FeNb$_3$Se$_{10}$

Non-magnetic FeNb$_3$Se$_{10}$ has been demonstrated to be an insulator at ambient pressure through both theoretical calculations and experimental measurements and it does not host topological surface states. Here we show that on the application of pressure, FeNb$_3$Se$_{10}$ transitions to a metallic state at around 3.0 GPa. With a further increase in pressure, its resistivity becomes independent of both temperature and pressure. Its crystal structure is maintained to at least 4.4 GPa.

cond-mat.str-el↗

arXiv2024

Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection

The rapid evolution of mobile networks from 5G to 6G has necessitated the development of autonomous network management systems, such as Zero-Touch Networks (ZTNs). However, the increased complexity and automation of these networks have also escalated cybersecurity risks. Existing Intrusion Detection Systems (IDSs) leveraging traditional Machine Learning (ML) techniques have shown effectiveness in mitigating these risks, but they often require extensive manual effort and expert knowledge. To address these challenges, this paper proposes an Automated Machine Learning (AutoML)-based autonomous IDS framework towards achieving autonomous cybersecurity for next-generation networks. To achieve autonomous intrusion detection, the proposed AutoML framework automates all critical procedures of the data analytics pipeline, including data pre-processing, feature engineering, model selection, hyperparameter tuning, and model ensemble. Specifically, it utilizes a Tabular Variational Auto-Encoder (TVAE) method for automated data balancing, tree-based ML models for automated feature selection and base model learning, Bayesian Optimization (BO) for hyperparameter optimization, and a novel Optimized Confidence-based Stacking Ensemble (OCSE) method for automated model ensemble. The proposed AutoML-based IDS was evaluated on two public benchmark network security datasets, CICIDS2017 and 5G-NIDD, and demonstrated improved performance compared to state-of-the-art cybersecurity methods. This research marks a significant step towards fully autonomous cybersecurity in next-generation networks, potentially revolutionizing network security applications.

cs.LG↗

arXiv2024

The exact lower bound of CNOT-complexity for fault-tolerant quantum Fourier transform

The quantum Fourier transform (QFT) is a crucial subroutine in many quantum algorithms. In this paper, we study the exact lower bound problem of CNOT gate complexity for fault-tolerant QFT. First, we consider approximating the ancilla-free controlled-$R_k$ in the QFT logical circuit with a standard set of universal gates, aiming to minimize the number of T gates. Various single-qubit gates are generated in addition to CNOT gates when the controlled-$R_k$ is decomposed in different ways, we propose an algorithm that combines numerical and analytical methods to exactly compute the minimum T gate count for approximating any single-qubit gate with any given accuracy. Afterwards, we prove that the exact lower bound problem of T gate complexity for the QFT is NP-complete. Furthermore, we provide the transversal implementation of universal quantum gates and prove that it has the minimum number of CNOT gates and analyze the minimum CNOT count for transversally implementing the T gate. We then exactly compute the exact lower bound of CNOT gate complexity for fault-tolerant QFT with the current maximum fault-tolerant accuracy 10^{-2}. Our work can provide a reference for designing algorithms based on active defense in a quantum setting.

quant-ph↗

arXiv2024

Above-room-temperature intrinsic ferromagnetism in ultrathin van der Waals crystal Fe$_{3+x}$GaTe$_2$

Two-dimensional (2D) van der Waals (vdW) magnets are crucial for ultra-compact spintronics. However, so far, no vdW crystal has exhibited tunable above-room-temperature intrinsic ferromagnetism in the 2D ultrathin regime. Here, we report the tunable above-room-temperature intrinsic ferromagnetism in ultrathin vdW crystal Fe$_{3+x}$GaTe$_2$ ($x$ = 0 and 0.3). By increasing the Fe content, the Curie temperature (TC) and room-temperature saturation magnetization of bulk Fe$_{3+x}$GaTe$_2$ crystals are enhanced from 354 to 376 K and 43.9 to 50.4 emu/g, respectively. Remarkably, the robust anomalous Hall effect in 3-nm Fe$_{3.3}$GaTe$_2$ indicate a record-high TC of 340 K and a large room-temperature perpendicular magnetic anisotropy energy of 6.6 * 10^5 J/m$^3$, superior to other ultrathin vdW ferromagnets. First-principles calculations reveal the asymmetric density of states and an additional large spin exchange interaction in ultrathin Fe$_{3+x}$GaTe$_2$ responsible for robust intrinsic ferromagnetism and higher Tc. This work opens a window for above-room-temperature ultrathin 2D magnets in vdW-integrated spintronics.

cond-mat.mtrl-sci↗

arXiv2024

On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case

As technology advances, the use of Machine Learning (ML) in cybersecurity is becoming increasingly crucial to tackle the growing complexity of cyber threats. While traditional ML models can enhance cybersecurity, their high energy and resource demands limit their applications, leading to the emergence of Tiny Machine Learning (TinyML) as a more suitable solution for resource-constrained environments. TinyML is widely applied in areas such as smart homes, healthcare, and industrial automation. TinyML focuses on optimizing ML algorithms for small, low-power devices, enabling intelligent data processing directly on edge devices. This paper provides a comprehensive review of common challenges of TinyML techniques, such as power consumption, limited memory, and computational constraints; it also explores potential solutions to these challenges, such as energy harvesting, computational optimization techniques, and transfer learning for privacy preservation. On the other hand, this paper discusses TinyML's applications in advancing cybersecurity for Electric Vehicle Charging Infrastructures (EVCIs) as a representative use case. It presents an experimental case study that enhances cybersecurity in EVCI using TinyML, evaluated against traditional ML in terms of reduced delay and memory usage, with a slight trade-off in accuracy. Additionally, the study includes a practical setup using the ESP32 microcontroller in the PlatformIO environment, which provides a hands-on assessment of TinyML's application in cybersecurity for EVCI.

cs.CR↗

arXiv2024

Quantum Truncated Differential and Boomerang Attack

Facing the worldwide steady progress in building quantum computers, it is crucial for cryptographic community to design quantum-safe cryptographic primitives. To achieve this, we need to investigate the capability of cryptographic analysis tools when used by the adversaries with quantum computers. In this article, we concentrate on truncated differential and boomerang cryptanalysis. We first present a quantum algorithm which is designed for finding truncated differentials of symmetric ciphers. We prove that, with a overwhelming probability, the truncated differentials output by our algorithm must have high differential probability for the vast majority of keys in key space. Afterwards, based on this algorithm, we design a quantum algorithm which can be used to find boomerang distinguishers. The quantum circuits of both quantum algorithms contain only polynomial quantum gates. Compared to classical tools for searching truncated differentials or boomerang distinguishers, our algorithms fully utilize the strengths of quantum computing, and can maintain the polynomial complexity while fully considering the impact of S-boxes and key scheduling.

quant-ph↗

arXiv2024

A Quantum Automatic Tool for Finding Impossible Differentials

Due to the superiority of quantum computing, traditional cryptography is facing severe threat. This makes the security evaluation of cryptographic systems in quantum attack models significant and urgent. For symmetric ciphers, the security analysis heavily relies on cyptanalytic tools. Thus exploring the use of quantum algorithms to traditional cyptanalytic tools has drawn a lot of attention. In this study, we utilize quantum algorithms to improve impossible differential attack, and design two quantum automatic tools for searching impossible differentials. The proposed quantum algorithms exploit the idea of miss-in-the-middle and the properties of truncated differentials. We rigorously prove their validity and calculate the quantum resources required to implement them. Compared to existing classical automatic cryptanalysis, the quantum tools proposed have the advantage of accurately characterizing S-boxes while only requiring polynomial complexity, and can take into consideration the impact of the key schedules in single-key model.

quant-ph↗

arXiv2024

Asymptotic properties of a multicolored random reinforced urn model with an application to multi-armed bandits

The random self-reinforcement mechanism, characterized by the principle of ``the rich get richer'', has demonstrated significant utility across various domains. One prominent model embodying this mechanism is the random reinforcement urn model. This paper investigates a multicolored, multiple-drawing variant of the random reinforced urn model. We establish the limiting behavior of the normalized urn composition and demonstrate strong convergence upon scaling the counts of each color. Additionally, we derive strong convergence estimators for the reinforcement means, i.e., for the expectations of the replacement matrix's diagonal elements, and prove their joint asymptotic normality. It is noteworthy that the estimators of the largest reinforcement mean are asymptotically independent of the estimators of the other smaller reinforcement means. Additionally, if a reinforcement mean is not the largest, the estimators of these smaller reinforcement means will also demonstrate asymptotic independence among themselves. Furthermore, we explore the parallels between the reinforced mechanisms in random reinforced urn models and multi-armed bandits, addressing hypothesis testing for expected payoffs in the latter context.

math.ST↗