arXiv subjects

Zeyu Liu

Publications and source records attributed to Zeyu Liu.

At least 73 records · Page 4Linked to original sources

arXiv2025

Semiregular tessellation of electronic lattices in untwisted bilayer graphene under anisotropic strain gradients

Two-dimensional (2D) moiré superlattices have emerged as a versatile platform for uncovering exotic quantum phases, many of which arise in bilayer systems exhibiting Archimedean tessellation patterns such as triangular, hexagonal, and kagome lattices. Here, we propose a strategy to engineer semiregular tessellation patterns in untwisted bilayer graphene by applying anisotropic epitaxial tensile strain (AETS) along crystallographic directions. Through force-field and first-principles calculations, we demonstrate that AETS can induce a rich variety of semiregular tessellation geometries, including truncated hextille, prismatic pentagon, and brick-phase arrangements. The characteristic electronic bands (Dirac and flat bands) of the lattice models associated with these semiregular tessellations are observed near the Fermi level, arising from interlayer interactions generated by the redistribution of specific stacking registries (AB, BA, and SP). Furthermore, the electronic kagome, distorted Lieb, brick-like, and one-dimensional stripe lattices captured in real-space confirm the tunable nature of the semiregular tessellation lattices enabled by AETS. Our study identifies AETS as a promising new degree of freedom in moiré engineering, offering a reproducible and scalable platform for exploring exotic electronic lattices in moiré systems.

cond-mat.mes-hall↗

arXiv2025

Rabi Oscillations of Strongly Driven Bose Polarons

Understanding the dynamical behavior of quasiparticles is essential for uncovering novel quantum many-body phenomena. Among these phenomena, the polaron in ultracold atomic gases has attracted considerable interest due to its precise controllability. By engineering the underlying Hamiltonian, polarons serve as a versatile platform for studying both equilibrium properties and non-equilibrium dynamics. In this work, we investigate the quantum dynamics of strongly driven Bose polarons, where minority atoms are described as mobile impurities with spin-1/2, and majority atoms are bosons. The spin-$\uparrow$ impurity interacts with majority atoms through a tunable scattering length $a$, while the spin-$\downarrow$ impurity remains non-interacting. After turning on the Rabi coupling, we calculate the evolution of the total magnetization using a trial wavefunction. We identify the exhibition of anomalous Rabi oscillation and steady-state magnetization for $a>0$, due to the interplay between attractive and repulsive polarons. Our results provide a concrete example that illustrates how Rabi oscillations are dressed by system-environment coupling.

cond-mat.quant-gas↗

arXiv2025

A stacky approach to prismatic crystals via $q$-prism charts

Let $Y$ be a locally complete intersection over $\mathcal{O}_K$ containing a $p$-power root of unity $ζ_p$. We classify the derived category of prismatic crystals on the absolute prismatic site of $Y$ by studying quasi-coherent complexes on the prismatization of $Y$ via $q$-prism charts. We also develop a Galois descent mechanism to remove the assumption on $\mathcal{O}_K$. As an application, we classify quasi-coherent complexes on the Cartier-Witt stack and give a purely algebraic calculation of the cohomology of the structure sheaf on the absolute prismatic site of $\mathbb{Z}_p$. Along the way, for $Y$ a locally complete intersection over $\overline{A}$ with $A$ lying over a $q$-prism, we classify quasi-coherent complexes on the relative prismatization of $Y$.

math.AG↗

arXiv2025

UnPuzzle: A Unified Framework for Pathology Image Analysis

Pathology image analysis plays a pivotal role in medical diagnosis, with deep learning techniques significantly advancing diagnostic accuracy and research. While numerous studies have been conducted to address specific pathological tasks, the lack of standardization in pre-processing methods and model/database architectures complicates fair comparisons across different approaches. This highlights the need for a unified pipeline and comprehensive benchmarks to enable consistent evaluation and accelerate research progress. In this paper, we present UnPuzzle, a novel and unified framework for pathological AI research that covers a broad range of pathology tasks with benchmark results. From high-level to low-level, upstream to downstream tasks, UnPuzzle offers a modular pipeline that encompasses data pre-processing, model composition,taskconfiguration,andexperimentconduction.Specifically, it facilitates efficient benchmarking for both Whole Slide Images (WSIs) and Region of Interest (ROI) tasks. Moreover, the framework supports variouslearningparadigms,includingself-supervisedlearning,multi-task learning,andmulti-modallearning,enablingcomprehensivedevelopment of pathology AI models. Through extensive benchmarking across multiple datasets, we demonstrate the effectiveness of UnPuzzle in streamlining pathology AI research and promoting reproducibility. We envision UnPuzzle as a cornerstone for future advancements in pathology AI, providing a more accessible, transparent, and standardized approach to model evaluation. The UnPuzzle repository is publicly available at https://github.com/Puzzle-AI/UnPuzzle.

eess.IV↗

arXiv2025

InfiR : Crafting Effective Small Language Models and Multimodal Small Language Models in Reasoning

Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have made significant advancements in reasoning capabilities. However, they still face challenges such as high computational demands and privacy concerns. This paper focuses on developing efficient Small Language Models (SLMs) and Multimodal Small Language Models (MSLMs) that retain competitive reasoning abilities. We introduce a novel training pipeline that enhances reasoning capabilities and facilitates deployment on edge devices, achieving state-of-the-art performance while minimizing development costs. \InfR~ aims to advance AI systems by improving reasoning, reducing adoption barriers, and addressing privacy concerns through smaller model sizes. Resources are available at https://github. com/Reallm-Labs/InfiR.

cs.CL↗

arXiv2025

The Case of FBA as a DEX Processing Model

We investigate the welfare loss of continuous and discrete order matching models in blockchain-based decentralized exchanges (DEX) that utilize order books to record outstanding orders. Continuous processing matches each incoming transaction against the current order book. The discrete processing model, i.e., frequent batch auction (FBA), executes transactions discretely in batches with a uniform price double auction: Orders are first matched according to price, then the exact transaction order if competing orders specify the same price. We find that FBA imposes less welfare loss and provides better liquidity than continuous processing in typical scenarios, e.g., when few parties are privately informed about asset valuations. Even otherwise, it achieves better social welfare and liquidity provision in the following settings: when price takers and public information reflecting asset value changes arrive sufficiently frequently compared to private information, when the priority fees (for faster transaction inclusion into blockchains) are small, or when the market is more balanced on both buy and sell sides. Our empirical analysis on the BTC-USD and ETH-USD transactions on a DEX named dYdX indicates that FBA can reduce transaction costs by $21\%-37\%$.

cs.CR↗

arXiv2025

Kagome electronic states in gradient-strained untwisted graphene bilayers

Moiré superlattices in twisted homo-bilayers have revealed exotic electronic states, including unconventional superconductivity and correlated insulating phases. However, their fabrication process often introduces moiré disorders, hindering reproducibility and experimental control. Here, we propose an alternative approach using gradient strain to construct moiré superlattices in untwisted bilayer graphene (gs-BLG). Through force-field and first-principles calculations, we show that gs-BLG exhibits kagome-like interlayer-spacing distributions and strain-tunable kagome electronic bands. The competition between interlayer coupling and in-plane strain relaxation leads to distinct structural deformations, giving rise to three forms of diatomic kagome lattices: subtle, pronounced, and distorted. Kagome electronic bands are identified near the Fermi level in their band structures. Modulating strain gradients enables tailoring bandwidths and signs of hopping parameters of these kagome bands, providing a versatile platform for studying exotic electronic phases. Our findings establish gradient strain as an alternative to twist engineering, opening an avenue for exploring emergent electronic phases in graphene-based systems.

cond-mat.mes-hall↗

arXiv2025

Scrambling Enabled Entropy Accumulation in Open Quantum Systems

In closed quantum many-body systems, initially localized information spreads throughout the system and becomes highly complex. This phenomenon, known as information scrambling, is closely related to entropy growth and quantum thermalization. Recent studies have shown that dissipation in open systems can hinder information scrambling, driving the system into a dissipative phase when the system-bath coupling is strong. However, the signature of this scrambling transition in entropy dynamics remains unexplored. In this work, we unveil a novel phenomenon in open quantum systems, termed entropy accumulation, which occurs exclusively within the scrambling phase. We consider a setup in which a probe is weakly coupled to a system that is already interacting with a bath. We calculate the increase in the second Rényi entropy induced by an external impulse on the system, after tracing out the probe. Despite the system-probe coupling being weak, the entropy continues to increase and eventually saturates at a finite value due to operator growth. In contrast, the entropy increase is limited by the coupling strength in the dissipative phase. The theoretical prediction is derived from both general arguments and an explicit example using generalized Boltzmann equations. Our results offer new insights into the intriguing relationship between entropy dynamics and information scrambling in open quantum systems.

quant-ph↗

arXiv2025

Mapping Transient Structures of Cyclo[18]Carbon by Computational X-Ray Spectra

The structure of cyclo[18]carbon (C$_{18}$), whether in its polyynic form with bond length alternation (BLA) or its cumulenic form without BLA, has long fascinated researchers, even prior to its successful synthesis. Recent studies suggest a polyynic ground state and a cumulenic transient state; however, the dynamics remain unclear and lack experimental validation. This study presents a first-principles theoretical investigation of the bond lengths ($R_1$ and $R_2$) dependent two-dimensional potential energy surfaces (PESs) of C$_{18}$, concentrating on the ground state and carbon 1s ionized and excited states. We examine the potential of X-ray spectra for determining bond lengths and monitoring transient structures, finding that both X-ray photoelectron (XPS) and absorption (XAS) spectra are sensitive to these variations. Utilizing a library of ground-state minimum structures optimized with 14 different functionals, we observe that core binding energies predicted with the $ω$B97XD functional can vary by 0.9 eV (290.3--291.2 eV). Unlike the ground state PES, which predicts minima at alternating bond lengths, the C1s ionized state PES predicts minima with equivalent bond lengths. In the XAS spectra, peaks 1$π^*$ and 2$π^*$ show a redshift with increasing bond lengths along the line where $R_1 = R_2$. Additionally, increasing $R_2$ (with $R_1$ fixed) results in an initial redshift followed by a blueshift, minimizing at $R_1 = R_2$. Major peaks indicate that both 1$π^*$ and 2$π^*$ arise from two channels: C1s$\rightarrowπ^*_{z}$ (out-of-plane) and C1s$\rightarrowπ^*_{xy}$ (in-plane) transitions at coinciding energies.

physics.chem-ph↗

arXiv2024

Fleximo: Towards Flexible Text-to-Human Motion Video Generation

Current methods for generating human motion videos rely on extracting pose sequences from reference videos, which restricts flexibility and control. Additionally, due to the limitations of pose detection techniques, the extracted pose sequences can sometimes be inaccurate, leading to low-quality video outputs. We introduce a novel task aimed at generating human motion videos solely from reference images and natural language. This approach offers greater flexibility and ease of use, as text is more accessible than the desired guidance videos. However, training an end-to-end model for this task requires millions of high-quality text and human motion video pairs, which are challenging to obtain. To address this, we propose a new framework called Fleximo, which leverages large-scale pre-trained text-to-3D motion models. This approach is not straightforward, as the text-generated skeletons may not consistently match the scale of the reference image and may lack detailed information. To overcome these challenges, we introduce an anchor point based rescale method and design a skeleton adapter to fill in missing details and bridge the gap between text-to-motion and motion-to-video generation. We also propose a video refinement process to further enhance video quality. A large language model (LLM) is employed to decompose natural language into discrete motion sequences, enabling the generation of motion videos of any desired length. To assess the performance of Fleximo, we introduce a new benchmark called MotionBench, which includes 400 videos across 20 identities and 20 motions. We also propose a new metric, MotionScore, to evaluate the accuracy of motion following. Both qualitative and quantitative results demonstrate that our method outperforms existing text-conditioned image-to-video generation methods. All code and model weights will be made publicly available.

cs.CV↗

arXiv2024

Diagnosing Strong-to-Weak Symmetry Breaking via Wightman Correlators

Symmetry plays a fundamental role in quantum many-body physics, and a central concept is spontaneous symmetry breaking, which imposes crucial constraints on the possible quantum phases and their transitions. Recent developments have extended the discussion of symmetry and its breaking to mixed states, enhancing our understanding of novel quantum phases that have no counterpart in pure states. Specific attention has been paid to scenarios where a strongly symmetric density matrix exhibits spontaneous symmetry breaking to weak symmetry, characterized by the fidelity correlator. In this work, we propose the Wightman correlator as an alternative diagnostic tool. This construction relies on the introduction of the thermofield double state for a generic density matrix, which maps the strong symmetry of the density matrix to the doubled symmetry of the pure state, allowing the Wightman correlator to emerge naturally as a standard probe of symmetry breaking. We prove the equivalence between the Wightman function and the fidelity correlator in defining strong-to-weak symmetry breaking, and examine explicit examples involving spin glasses, thermal density matrices, and the decohered Ising model. Additionally, we discuss a susceptibility interpretation of the Wightman correlator.

quant-ph↗

arXiv2024

Experimental observation of ballistic to diffusive transition in AlN thin films

Bulk AlN possesses high thermal conductivity due to long phonon mean-free-paths, high group velocity, and long lifetimes. However, the thermal transport scenario becomes very different in a thin AlN film due to phonon-defect and phonon-boundary scattering. Herein, we report experimental observation of ballistic to diffusive transition in a series of AlN thin films (1.6 - 2440 nm) grown on sapphire substrates. The ballistic transport is characterized by constant thermal resistance as a function of film thickness due to phonon scattering by defects and boundaries. In this transport regime, phonons possess very small group velocities and lifetimes. The lifetime of the optical phonons increases by more than an order of magnitude in the diffusive regime, however, remains nearly constant afterwards. Our study is important for understanding the details of nano and microscale thermal transport in a highly conductive material.

physics.app-ph↗

arXiv2024

On the prismatization of $O_K$ beyond the Hodge-Tate locus

Let $X=\mathrm{Spf}(\mathcal{O}_K)$. We classify perfect complexes of $n$-truncated prismatic crystals on the prismatic site of $X$ when $n\leq 1+\frac{p-1}{e}$ by studying perfect complexes on the $n$-truncated prismatization of $X$, which are certain nilpotent thickenings of the Hodge-Tate stack of $X$ inside the prismatization of $X$. We describe the category of continuous semilinear representations and their cohomology for $G_K$ with coefficients in $B_{\mathrm{dR},n}^+$ via rationalization of vector bundles on the slight shrinking of the $n$-truncated prismatization of $X$. Along the way, we classify certain integral models for de Rham prismatic crystals studied in arXiv:2205.14914.

math.AG↗

arXiv2024

Dynamic Pricing of Electric Vehicle Charging Station Alliances Under Information Asymmetry

Due to the centralization of charging stations (CSs), CSs are organized as charging station alliances (CSAs) in the commercial competition. Under this situation, this paper studies the profit-oriented dynamic pricing strategy of CSAs. As the practicability basis, a privacy-protected bidirectional real-time information interaction framework is designed, under which the status of EVs is utilized as the reference for pricing, and the prices of CSs are the reference for charging decisions. Based on this framework, the decision-making models of EVs and CSs are established, in which the uncertainty caused by the information asymmetry between EVs and CSs and the bounded rationality of EV users are integrated. To solve the pricing decision model, the evolutionary game theory is adopted to describe the dynamic pricing game among CSAs, the equilibrium of which gives the optimal pricing strategy. Finally, the case study results in a real urban area in Shanghai, China verifies the practicability of the framework and the effectiveness of the dynamic pricing strategy.

eess.SY↗

arXiv2024

Efficient automatic segmentation for multi-level pulmonary arteries: The PARSE challenge

Efficient automatic segmentation of multi-level (i.e. main and branch) pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications. However, most existing methods concentrate only on main PA or branch PA segmentation separately and ignore segmentation efficiency. Besides, there is no public large-scale dataset focused on PA segmentation, which makes it highly challenging to compare the different methods. To benchmark multi-level PA segmentation algorithms, we organized the first \textbf{P}ulmonary \textbf{AR}tery \textbf{SE}gmentation (PARSE) challenge. On the one hand, we focus on both the main PA and the branch PA segmentation. On the other hand, for better clinical application, we assign the same score weight to segmentation efficiency (mainly running time and GPU memory consumption during inference) while ensuring PA segmentation accuracy. We present a summary of the top algorithms and offer some suggestions for efficient and accurate multi-level PA automatic segmentation. We provide the PARSE challenge as open-access for the community to benchmark future algorithm developments at \url{https://parse2022.grand-challenge.org/Parse2022/}.

eess.IV↗

arXiv2024

Enhanced Prediction of Multi-Agent Trajectories via Control Inference and State-Space Dynamics

In the field of autonomous systems, accurately predicting the trajectories of nearby vehicles and pedestrians is crucial for ensuring both safety and operational efficiency. This paper introduces a novel methodology for trajectory forecasting based on state-space dynamic system modeling, which endows agents with models that have tangible physical implications. To enhance the precision of state estimations within the dynamic system, the paper also presents a novel modeling technique for control variables. This technique utilizes a newly introduced model, termed "Mixed Mamba," to derive initial control states, thereby improving the predictive accuracy of these variables. Moverover, the proposed approach ingeniously integrates graph neural networks with state-space models, effectively capturing the complexities of multi-agent interactions. This combination provides a robust and scalable framework for forecasting multi-agent trajectories across a range of scenarios. Comprehensive evaluations demonstrate that this model outperforms several established benchmarks across various metrics and datasets, highlighting its significant potential to advance trajectory forecasting in autonomous systems.

cs.RO↗

arXiv2024

CrudiTEE: A Stick-and-Carrot Approach to Building Trustworthy Cryptocurrency Wallets with TEEs

Cryptocurrency introduces usability challenges by requiring users to manage signing keys. Popular signing key management services (e.g., custodial wallets), however, either introduce a trusted party or burden users with managing signing key shares, posing the same usability challenges. TEEs (Trusted Execution Environments) are a promising technology to avoid both, but practical implementations of TEEs suffer from various side-channel attacks that have proven hard to eliminate. This paper explores a new approach to side-channel mitigation through economic incentives for TEE-based cryptocurrency wallet solutions. By taking the cost and profit of side-channel attacks into consideration, we designed a Stick-and-Carrot-based cryptocurrency wallet, CrudiTEE, that leverages penalties (the stick) and rewards (the carrot) to disincentivize attackers from exfiltrating signing keys in the first place. We model the attacker's behavior using a Markov Decision Process (MDP) to evaluate the effectiveness of the bounty and enable the service provider to adjust the parameters of the bounty's reward function accordingly.

cs.CR↗

arXiv2024

Glyph-ByT5: A Customized Text Encoder for Accurate Visual Text Rendering

Visual text rendering poses a fundamental challenge for contemporary text-to-image generation models, with the core problem lying in text encoder deficiencies. To achieve accurate text rendering, we identify two crucial requirements for text encoders: character awareness and alignment with glyphs. Our solution involves crafting a series of customized text encoder, Glyph-ByT5, by fine-tuning the character-aware ByT5 encoder using a meticulously curated paired glyph-text dataset. We present an effective method for integrating Glyph-ByT5 with SDXL, resulting in the creation of the Glyph-SDXL model for design image generation. This significantly enhances text rendering accuracy, improving it from less than $20\%$ to nearly $90\%$ on our design image benchmark. Noteworthy is Glyph-SDXL's newfound ability for text paragraph rendering, achieving high spelling accuracy for tens to hundreds of characters with automated multi-line layouts. Finally, through fine-tuning Glyph-SDXL with a small set of high-quality, photorealistic images featuring visual text, we showcase a substantial improvement in scene text rendering capabilities in open-domain real images. These compelling outcomes aim to encourage further exploration in designing customized text encoders for diverse and challenging tasks.

cs.CV↗