arXiv subjects

Lei Liao

Publications and source records attributed to Lei Liao.

At least 19 recordsLinked to original sources

arXiv2026

On the Nonlinear Dependence of Underground Muon Rate on Atmospheric Temperature Observed at Daya Bay

The underground cosmic-ray muon rate is known to be modulated by atmospheric temperature. It can be explained by the theories of Barrett, Gaisser, and others. However, at the Daya Bay Neutrino Experiment, the dependence on temperature is observed to be nonlinear. We found that, when deriving the temperature dependence of muon rate, existing theories only consider the impact of local temperature on muon production at the layer where muons are produced. In this work, we provide an more general solution to the cascade equations, which is complex enough to fully depict how entire temperautre profile would influence the final muon rate. Corresponding definitions of the effective temperature weight and temperature coefficient are also presented. We examine the results with numerical tool MCEq and real atmospheric temperature input. A linear modulation is recovered and verified. This work can help to explain the nonlinear effect found at Daya Bay, and provide a more refined calculation framework for temperature coefficient calculation for other experiments.

astro-ph.IM↗

arXiv2026

Kinetics of Stacking Order Evolution During Heterogeneous Ice Formation

The selection of stacking order in a broad range of close-packed polymorphic materials remains a challenging enigma. Using in situ cryogenic transmission electron microscopy, we uncover the atomistic mechanisms governing the vapour deposition growth of ice. We find that the heterogeneous ice nucleation and growth undergoes recrystallization accompanied by bifurcation, reflecting a coherent epitaxial transition from a cubic-ice embryonic core to hexagonal-ice prismatic dendrites, with intermediate stacking-disordered layers serving as a dynamic fluctuating bridge. Supported by molecular dynamics simulations, these phenomena are attributed to a surface-constrained, symmetry-breaking crystallization preference aligned with the principle of minimizing free energy. Our results highlight the critical role of the combined effects of surface and symmetry in shaping ice crystallization, providing fresh insights into crystal growth mechanisms and guiding principles for the design of advanced materials.

cond-mat.mtrl-sci↗

arXiv2026

Dolphin-v2: Universal Document Parsing via Scalable Anchor Prompting

Document parsing has garnered widespread attention as vision-language models (VLMs) advance OCR capabilities. However, the field remains fragmented across dozens of specialized models with varying strengths, forcing users to navigate complex model selection and limiting system scalability. Moreover, existing two-stage approaches depend on axis-aligned bounding boxes for layout detection, failing to handle distorted or photographed documents effectively. To this end, we present Dolphin-v2, a two-stage document image parsing model that substantially improves upon the original Dolphin. In the first stage, Dolphin-v2 jointly performs document type classification (digital-born versus photographed) alongside layout analysis. For digital-born documents, it conducts finer-grained element detection with reading order prediction. In the second stage, we employ a hybrid parsing strategy: photographed documents are parsed holistically as complete pages to handle geometric distortions, while digital-born documents undergo element-wise parallel parsing guided by the detected layout anchors, enabling efficient content extraction. Compared with the original Dolphin, Dolphin-v2 introduces several crucial enhancements: (1) robust parsing of photographed documents via holistic page-level understanding, (2) finer-grained element detection (21 categories) with semantic attribute extraction such as author information and document metadata, and (3) code block recognition with indentation preservation, which existing systems typically lack. Comprehensive evaluations are conducted on DocPTBench, OmniDocBench, and our self-constructed RealDoc-160 benchmark. The results demonstrate substantial improvements: +14.78 points overall on the challenging OmniDocBench and 91% error reduction on photographed documents, while maintaining efficient inference through parallel processing.

cs.CV↗

arXiv2025

From Ferromagnet to Antiferromagnet: Dimensional Crossover in (111) SrRuO3 Ultrathin Films

SrRuO3 is a canonical itinerant ferromagnet, yet its properties in the extreme two-dimensional limit on a (111) crystal plane remain largely unexplored. Here, we demonstrate a complete transformation of its ground state driven by dimensional reduction. As the thickness of (111)-oriented SrRuO3 films is reduced to a few unit cells, the system transitions from a metallic ferromagnet to a semiconducting antiferromagnet. This emergent antiferromagnetism is evidenced by a vanishing magnetic remanence and most strikingly, by the appearance of an unconventional twelve-fold anisotropic magnetoresistance. First-principles calculations confirm that an A-type antiferromagnetic order is the stable ground state in the ultrathin limit. Our findings establish (111) dimensional engineering as a powerful route to manipulate correlated electron states and uncover novel functionalities for antiferromagnetic spintronics.

cond-mat.str-el↗

arXiv2025

Benchmarking Vision-Language Models on Chinese Ancient Documents: From OCR to Knowledge Reasoning

Chinese ancient documents, invaluable carriers of millennia of Chinese history and culture, hold rich knowledge across diverse fields but face challenges in digitization and understanding, i.e., traditional methods only scan images, while current Vision-Language Models (VLMs) struggle with their visual and linguistic complexity. Existing document benchmarks focus on English printed texts or simplified Chinese, leaving a gap for evaluating VLMs on ancient Chinese documents. To address this, we present AncientDoc, the first benchmark for Chinese ancient documents, designed to assess VLMs from OCR to knowledge reasoning. AncientDoc includes five tasks (page-level OCR, vernacular translation, reasoning-based QA, knowledge-based QA, linguistic variant QA) and covers 14 document types, over 100 books, and about 3,000 pages. Based on AncientDoc, we evaluate mainstream VLMs using multiple metrics, supplemented by a human-aligned large language model for scoring.

cs.CL↗

arXiv2025

Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting

Document image parsing is challenging due to its complexly intertwined elements such as text paragraphs, figures, formulas, and tables. Current approaches either assemble specialized expert models or directly generate page-level content autoregressively, facing integration overhead, efficiency bottlenecks, and layout structure degradation despite their decent performance. To address these limitations, we present \textit{Dolphin} (\textit{\textbf{Do}cument Image \textbf{P}arsing via \textbf{H}eterogeneous Anchor Prompt\textbf{in}g}), a novel multimodal document image parsing model following an analyze-then-parse paradigm. In the first stage, Dolphin generates a sequence of layout elements in reading order. These heterogeneous elements, serving as anchors and coupled with task-specific prompts, are fed back to Dolphin for parallel content parsing in the second stage. To train Dolphin, we construct a large-scale dataset of over 30 million samples, covering multi-granularity parsing tasks. Through comprehensive evaluations on both prevalent benchmarks and self-constructed ones, Dolphin achieves state-of-the-art performance across diverse page-level and element-level settings, while ensuring superior efficiency through its lightweight architecture and parallel parsing mechanism. The code and pre-trained models are publicly available at https://github.com/ByteDance/Dolphin

cs.CV↗

arXiv2025

RViDeformer: Efficient Raw Video Denoising Transformer with a Larger Benchmark Dataset

In recent years, raw video denoising has garnered increased attention due to the consistency with the imaging process and well-studied noise modeling in the raw domain. However, two problems still hinder the denoising performance. Firstly, there is no large dataset with realistic motions for supervised raw video denoising, as capturing noisy and clean frames for real dynamic scenes is difficult. To address this, we propose recapturing existing high-resolution videos displayed on a 4K screen with high-low ISO settings to construct noisy-clean paired frames. In this way, we construct a video denoising dataset (named as ReCRVD) with 120 groups of noisy-clean videos, whose ISO values ranging from 1600 to 25600. Secondly, while non-local temporal-spatial attention is beneficial for denoising, it often leads to heavy computation costs. We propose an efficient raw video denoising transformer network (RViDeformer) that explores both short and long-distance correlations. Specifically, we propose multi-branch spatial and temporal attention modules, which explore the patch correlations from local window, local low-resolution window, global downsampled window, and neighbor-involved window, and then they are fused together. We employ reparameterization to reduce computation costs. Our network is trained in both supervised and unsupervised manners, achieving the best performance compared with state-of-the-art methods. Additionally, the model trained with our proposed dataset (ReCRVD) outperforms the model trained with previous benchmark dataset (CRVD) when evaluated on the real-world outdoor noisy videos. Our code and dataset are available at https://github.com/cao-cong/RViDeformer.

cs.CV↗

arXiv2025

ParGo: Bridging Vision-Language with Partial and Global Views

This work presents ParGo, a novel Partial-Global projector designed to connect the vision and language modalities for Multimodal Large Language Models (MLLMs). Unlike previous works that rely on global attention-based projectors, our ParGo bridges the representation gap between the separately pre-trained vision encoders and the LLMs by integrating global and partial views, which alleviates the overemphasis on prominent regions. To facilitate the effective training of ParGo, we collect a large-scale detail-captioned image-text dataset named ParGoCap-1M-PT, consisting of 1 million images paired with high-quality captions. Extensive experiments on several MLLM benchmarks demonstrate the effectiveness of our ParGo, highlighting its superiority in aligning vision and language modalities. Compared to conventional Q-Former projector, our ParGo achieves an improvement of 259.96 in MME benchmark. Furthermore, our experiments reveal that ParGo significantly outperforms other projectors, particularly in tasks that emphasize detail perception ability.

cs.CV↗

arXiv2024

Layer-dependent evolution of electronic structures and correlations in rhombohedral multilayer graphene

The recent discovery of superconductivity and magnetism in trilayer rhombohedral graphene (RG) establishes an ideal, untwisted platform to study strong correlation electronic phenomena. However, the correlated effects in multilayer RG have received limited attention, and, particularly, the evolution of the correlations with increasing layer number remains an unresolved question. Here, we show the observation of layer-dependent electronic structures and correlations, under surprising liquid nitrogen temperature, in RG multilayers from 3 to 9 layers by using scanning tunneling microscopy and spectroscopy. We explicitly determine layer-enhanced low-energy flat bands and interlayer coupling strengths. The former directly demonstrates the further flattening of low-energy bands in thicker RG, and the latter indicates the presence of varying interlayer interactions in RG multilayers. Moreover, we find significant splittings of the flat bands, ranging from ~50-80 meV, at 77 K when they are partially filled, indicating the emergence of interaction-induced strongly correlated states. Particularly, the strength of the correlated states is notably enhanced in thicker RG and reaches its maximum in the six-layer, validating directly theoretical predictions and establishing abundant new candidates for strongly correlated systems. Our results provide valuable insights into the layer dependence of the electronic properties in RG and demonstrate it as a suitable system for investigating robust and highly accessible correlated phases.

cond-mat.mes-hall↗

arXiv2024

MCTBench: Multimodal Cognition towards Text-Rich Visual Scenes Benchmark

The comprehension of text-rich visual scenes has become a focal point for evaluating Multi-modal Large Language Models (MLLMs) due to their widespread applications. Current benchmarks tailored to the scenario emphasize perceptual capabilities, while overlooking the assessment of cognitive abilities. To address this limitation, we introduce a Multimodal benchmark towards Text-rich visual scenes, to evaluate the Cognitive capabilities of MLLMs through visual reasoning and content-creation tasks (MCTBench). To mitigate potential evaluation bias from the varying distributions of datasets, MCTBench incorporates several perception tasks (e.g., scene text recognition) to ensure a consistent comparison of both the cognitive and perceptual capabilities of MLLMs. To improve the efficiency and fairness of content-creation evaluation, we conduct an automatic evaluation pipeline. Evaluations of various MLLMs on MCTBench reveal that, despite their impressive perceptual capabilities, their cognition abilities require enhancement. We hope MCTBench will offer the community an efficient resource to explore and enhance cognitive capabilities towards text-rich visual scenes.

cs.CV↗

arXiv2024

TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy

Tables contain factual and quantitative data accompanied by various structures and contents that pose challenges for machine comprehension. Previous methods generally design task-specific architectures and objectives for individual tasks, resulting in modal isolation and intricate workflows. In this paper, we present a novel large vision-language model, TabPedia, equipped with a concept synergy mechanism. In this mechanism, all the involved diverse visual table understanding (VTU) tasks and multi-source visual embeddings are abstracted as concepts. This unified framework allows TabPedia to seamlessly integrate VTU tasks, such as table detection, table structure recognition, table querying, and table question answering, by leveraging the capabilities of large language models (LLMs). Moreover, the concept synergy mechanism enables table perception-related and comprehension-related tasks to work in harmony, as they can effectively leverage the needed clues from the corresponding source perception embeddings. Furthermore, to better evaluate the VTU task in real-world scenarios, we establish a new and comprehensive table VQA benchmark, ComTQA, featuring approximately 9,000 QA pairs. Extensive quantitative and qualitative experiments on both table perception and comprehension tasks, conducted across various public benchmarks, validate the effectiveness of our TabPedia. The superior performance further confirms the feasibility of using LLMs for understanding visual tables when all concepts work in synergy. The benchmark ComTQA has been open-sourced at https://huggingface.co/datasets/ByteDance/ComTQA. The source code and model also have been released athttps://github.com/zhaowc-ustc/TabPedia.

cs.CV↗

arXiv2024

TextSquare: Scaling up Text-Centric Visual Instruction Tuning

Text-centric visual question answering (VQA) has made great strides with the development of Multimodal Large Language Models (MLLMs), yet open-source models still fall short of leading models like GPT4V and Gemini, partly due to a lack of extensive, high-quality instruction tuning data. To this end, we introduce a new approach for creating a massive, high-quality instruction-tuning dataset, Square-10M, which is generated using closed-source MLLMs. The data construction process, termed Square, consists of four steps: Self-Questioning, Answering, Reasoning, and Evaluation. Our experiments with Square-10M led to three key findings: 1) Our model, TextSquare, considerably surpasses open-source previous state-of-the-art Text-centric MLLMs and sets a new standard on OCRBench(62.2%). It even outperforms top-tier models like GPT4V and Gemini in 6 of 10 text-centric benchmarks. 2) Additionally, we demonstrate the critical role of VQA reasoning data in offering comprehensive contextual insights for specific questions. This not only improves accuracy but also significantly mitigates hallucinations. Specifically, TextSquare scores an average of 75.1% across four general VQA and hallucination evaluation datasets, outperforming previous state-of-the-art models. 3) Notably, the phenomenon observed in scaling text-centric VQA datasets reveals a vivid pattern: the exponential increase of instruction tuning data volume is directly proportional to the improvement in model performance, thereby validating the necessity of the dataset scale and the high quality of Square-10M.

cs.CV↗

arXiv2022

Fe-assisted epitaxial growth of 4-inch single-crystal transition-metal dichalcogenides on c-plane sapphire without miscut angle

Epitaxial growth and controllable doping of wafer-scale single-crystal transition-metal dichalcogenides (TMDCs) are two central tasks for extending Moore's law beyond silicon. However, despite considerable efforts, addressing such crucial issues simultaneously under two-dimensional (2D) confinement is yet to be realized. Here we design an ingenious epitaxial strategy to synthesize record-breaking 4-inch single-crystal Fe-doped TMDCs monolayers on industry-compatible c-plane sapphire without miscut angle. In-depth characterizations and theoretical calculations reveal that the introduction of Fe significantly decreases the formation energy of parallel steps on sapphire surfaces and contributes to the edge-nucleation of unidirectional TMDCs domains (>99%). The ultrahigh electron mobility (~86 cm2 V -1 s-1) and remarkable on/off current ratio (~108) are discovered on 4-inch single-crystal Fe-MoS2 monolayers due to the ultralow contact resistance and perfect Ohmic contact with metal electrodes. This work represents a substantial leap in terms of bridging the synthesis and doping of wafer-scale single-crystal 2D semiconductors without the need for substrate miscut, which should promote the further device downscaling and extension of Moore's law.

physics.app-ph↗

arXiv2022

Emergence of insulating ferrimagnetism and perpendicular magnetic anisotropy in 3d-5d perovskite oxide composite films for insulator spintronic

Magnetic insulators with strong perpendicular magnetic anisotropy (PMA) play a key role in exploring pure spin current phenomena and developing ultralow-dissipation spintronic devices, thereby it is highly desirable to develop new material platforms. Here we report epitaxial growth of La2/3Sr1/3MnO3 (LSMO)-SrIrO3 (SIO) composite oxide films (LSMIO) with different crystalline orientations fabricated by sequential two-target ablation process using pulsed laser deposition. The LSMIO films exhibit high crystalline quality with homogeneous mixture of LSMO and SIO at atomic level. Ferrimagnetic and insulating transport characteristics are observed, with the temperature-dependent electric resistivity well fitted by Mott variable-range-hopping model. Moreover, the LSMIO films show strong PMA. Through further constructing all perovskite oxide heterostructures of the ferrimagnetic insulator LSMIO and a strong spin-orbital coupled SIO layer, pronounced spin Hall magnetoresistance (SMR) and spin Hall-like anomalous Hall effect (SH-AHE) were observed. These results illustrate the potential application of the ferrimagnetic insulator LSMIO in developing all-oxide ultralow-dissipation spintronic devices.

cond-mat.mtrl-sci↗

arXiv2022

Universal and Efficient p-Doping of Organic Semiconductors by Electrophilic Attack of Cations

Doping is of great importance to tailor the electrical properties of semiconductors. However, the present doping methodologies for organic semiconductors (OSCs) are either inefficient or can only apply to a small number of OSCs, seriously limiting their general application. Herein, we reveal a novel p-doping mechanism by investigating the interactions between the dopant trityl cation and poly(3-hexylthiophene) (P3HT). It is found that electrophilic attack of the trityl cations on thiophenes results in the formation of alkylated ions that induce electron transfer from neighboring P3HT chains, resulting in p-doping. This unique p-doping mechanism can be employed to dope various OSCs including those with high ionization energy (IE=5.8 eV). Moreover, this doping mechanism endows trityl cation with strong doping ability, leading to polaron yielding efficiency of 100 % and doping efficiency of over 80 % in P3HT. The discovery and elucidation of this novel doping mechanism not only points out that strong electrophiles are a class of efficient p-dopants for OSCs, but also provides new opportunities towards highly efficient doping of OSCs.

cond-mat.mtrl-sci↗

arXiv2021

DSBERT:Unsupervised Dialogue Structure learning with BERT

Unsupervised dialogue structure learning is an important and meaningful task in natural language processing. The extracted dialogue structure and process can help analyze human dialogue, and play a vital role in the design and evaluation of dialogue systems. The traditional dialogue system requires experts to manually design the dialogue structure, which is very costly. But through unsupervised dialogue structure learning, dialogue structure can be automatically obtained, reducing the cost of developers constructing dialogue process. The learned dialogue structure can be used to promote the dialogue generation of the downstream task system, and improve the logic and consistency of the dialogue robot's reply.In this paper, we propose a Bert-based unsupervised dialogue structure learning algorithm DSBERT (Dialogue Structure BERT). Different from the previous SOTA models VRNN and SVRNN, we combine BERT and AutoEncoder, which can effectively combine context information. In order to better prevent the model from falling into the local optimal solution and make the dialogue state distribution more uniform and reasonable, we also propose three balanced loss functions that can be used for dialogue structure learning. Experimental results show that DSBERT can generate a dialogue structure closer to the real structure, can distinguish sentences with different semantics and map them to different hidden states.

cs.CL↗

arXiv2021

Manipulation of polar vortex chirality in oxide superlattices

Topological polar vortices that are the electric analogues of magnetic objects, present great potential in applications of future nanoelectronics due to their nanometer size, anomalous dielectric response, and chirality. To enable the functionalities, it is prerequisite to manipulate the polar states and chirality by using external stimuli. Here, we probe the evolutions of polar state and chirality of polar vortices in PbTiO3/SrTiO3 superlattices under electric field by using atomically resolved in situ scanning transmission electron microscopy and phase-field simulations. We find that the adjacent clockwise and counterclockwise vortex usually have opposite chirality. The phase-field simulations suggest that the rotation reversal or axial polarization switching can lead to the chirality change. Guided by which, we experimentally validate that the vortex rotation direction can be changed by applying and subsequently removing of electric fields, offering a potential strategy to manipulate the vortex chirality. The revealed details of dynamic behavior for individual polar vortices at atomic scale and the proposed strategy for chirality manipulation provide fundamentals for future device applications.

cond-mat.mtrl-sci↗

arXiv2020

Supervised Raw Video Denoising with a Benchmark Dataset on Dynamic Scenes

In recent years, the supervised learning strategy for real noisy image denoising has been emerging and has achieved promising results. In contrast, realistic noise removal for raw noisy videos is rarely studied due to the lack of noisy-clean pairs for dynamic scenes. Clean video frames for dynamic scenes cannot be captured with a long-exposure shutter or averaging multi-shots as was done for static images. In this paper, we solve this problem by creating motions for controllable objects, such as toys, and capturing each static moment for multiple times to generate clean video frames. In this way, we construct a dataset with 55 groups of noisy-clean videos with ISO values ranging from 1600 to 25600. To our knowledge, this is the first dynamic video dataset with noisy-clean pairs. Correspondingly, we propose a raw video denoising network (RViDeNet) by exploring the temporal, spatial, and channel correlations of video frames. Since the raw video has Bayer patterns, we pack it into four sub-sequences, i.e RGBG sequences, which are denoised by the proposed RViDeNet separately and finally fused into a clean video. In addition, our network not only outputs a raw denoising result, but also the sRGB result by going through an image signal processing (ISP) module, which enables users to generate the sRGB result with their favourite ISPs. Experimental results demonstrate that our method outperforms state-of-the-art video and raw image denoising algorithms on both indoor and outdoor videos.

eess.IV↗