arXiv subjects

Yu Zhou

Publications and source records attributed to Yu Zhou.

At least 217 records · Page 12Linked to original sources

arXiv2024

Typical Algorithms for Estimating Hurst Exponent of Time Sequence: A Data Analyst's Perspective

The Hurst exponent is a significant metric for characterizing time sequences with long-term memory property and it arises in many fields. The available methods for estimating the Hurst exponent can be categorized into time-domain and spectrum-domain methods. Although there are various estimation methods for the Hurst exponent, there are still some disadvantages that should be overcome: firstly, the estimation methods are mathematics-oriented instead of engineering-oriented; secondly, the accuracy and effectiveness of the estimation algorithms are inadequately assessed; thirdly, the framework of classification for the estimation methods are insufficient; and lastly there is a lack of clear guidance for selecting proper estimation in practical problems involved in data analysis. The contributions of this paper lie in four aspects: 1) the optimal sequence partition method is proposed for designing the estimation algorithms for Hurst exponent; 2) the algorithmic pseudo-codes are adopted to describe the estimation algorithms, which improves the understandability and usability of the estimation methods and also reduces the difficulty of implementation with computer programming languages; 3) the performance assessment is carried for the typical estimation algorithms via the ideal time sequence with given Hurst exponent and the practical time sequence captured in applications; 4) the guidance for selecting proper algorithms for estimating the Hurst exponent is presented and discussed. It is expected that the systematic survey of available estimation algorithms could help the users to understand the principles and the assessment of the various estimation methods could help the users to select, implement and apply the estimation algorithms of interest in practical situations in an easy way.

stat.ME↗

arXiv2024

LDP: Generalizing to Multilingual Visual Information Extraction by Language Decoupled Pretraining

Visual Information Extraction (VIE) plays a crucial role in the comprehension of semi-structured documents, and several pre-trained models have been developed to enhance performance. However, most of these works are monolingual (usually English). Due to the extremely unbalanced quantity and quality of pre-training corpora between English and other languages, few works can extend to non-English scenarios. In this paper, we conduct systematic experiments to show that vision and layout modality hold invariance among images with different languages. If decoupling language bias from document images, a vision-layout-based model can achieve impressive cross-lingual generalization. Accordingly, we present a simple but effective multilingual training paradigm LDP (Language Decoupled Pre-training) for better utilization of monolingual pre-training data. Our proposed model LDM (Language Decoupled Model) is first pre-trained on the language-independent data, where the language knowledge is decoupled by a diffusion model, and then the LDM is fine-tuned on the downstream languages. Extensive experiments show that the LDM outperformed all SOTA multilingual pre-trained models, and also maintains competitiveness on downstream monolingual/English benchmarks.

cs.CV↗

arXiv2024

SimADFuzz: Simulation-Feedback Fuzz Testing for Autonomous Driving Systems

Autonomous driving systems (ADS) have achieved remarkable progress in recent years. However, ensuring their safety and reliability remains a critical challenge due to the complexity and uncertainty of driving scenarios. In this paper, we focus on simulation testing for ADS, where generating diverse and effective testing scenarios is a central task. Existing fuzz testing methods face limitations, such as overlooking the temporal and spatial dynamics of scenarios and failing to leverage simulation feedback (e.g., speed, acceleration and heading) to guide scenario selection and mutation. To address these issues, we propose SimADFuzz, a novel framework designed to generate high-quality scenarios that reveal violations in ADS behavior. Specifically, SimADFuzz employs violation prediction models, which evaluate the likelihood of ADS violations, to optimize scenario selection. Moreover, SimADFuzz proposes distance-guided mutation strategies to enhance interactions among vehicles in offspring scenarios, thereby triggering more edge-case behaviors of vehicles. Comprehensive experiments demonstrate that SimADFuzz outperforms state-of-the-art fuzzers by identifying 32 more unique violations, including 4 reproducible cases of vehicle-vehicle and vehicle-pedestrian collisions. These results demonstrate SimADFuzz's effectiveness in enhancing the robustness and safety of autonomous driving systems.

cs.SE↗

arXiv2024

Track the Answer: Extending TextVQA from Image to Video with Spatio-Temporal Clues

Video text-based visual question answering (Video TextVQA) is a practical task that aims to answer questions by jointly reasoning textual and visual information in a given video. Inspired by the development of TextVQA in image domain, existing Video TextVQA approaches leverage a language model (e.g. T5) to process text-rich multiple frames and generate answers auto-regressively. Nevertheless, the spatio-temporal relationships among visual entities (including scene text and objects) will be disrupted and models are susceptible to interference from unrelated information, resulting in irrational reasoning and inaccurate answering. To tackle these challenges, we propose the TEA (stands for ``\textbf{T}rack th\textbf{E} \textbf{A}nswer'') method that better extends the generative TextVQA framework from image to video. TEA recovers the spatio-temporal relationships in a complementary way and incorporates OCR-aware clues to enhance the quality of reasoning questions. Extensive experiments on several public Video TextVQA datasets validate the effectiveness and generalization of our framework. TEA outperforms existing TextVQA methods, video-language pretraining methods and video large language models by great margins.

cs.CV↗

arXiv2024

Arbitrary Reading Order Scene Text Spotter with Local Semantics Guidance

Scene text spotting has attracted the enthusiasm of relative researchers in recent years. Most existing scene text spotters follow the detection-then-recognition paradigm, where the vanilla detection module hardly determines the reading order and leads to failure recognition. After rethinking the auto-regressive scene text recognition method, we find that a well-trained recognizer can implicitly perceive the local semantics of all characters in a complete word or a sentence without a character-level detection module. Local semantic knowledge not only includes text content but also spatial information in the right reading order. Motivated by the above analysis, we propose the Local Semantics Guided scene text Spotter (LSGSpotter), which auto-regressively decodes the position and content of characters guided by the local semantics. Specifically, two effective modules are proposed in LSGSpotter. On the one hand, we design a Start Point Localization Module (SPLM) for locating text start points to determine the right reading order. On the other hand, a Multi-scale Adaptive Attention Module (MAAM) is proposed to adaptively aggregate text features in a local area. In conclusion, LSGSpotter achieves the arbitrary reading order spotting task without the limitation of sophisticated detection, while alleviating the cost of computational resources with the grid sampling strategy. Extensive experiment results show LSGSpotter achieves state-of-the-art performance on the InverseText benchmark. Moreover, our spotter demonstrates superior performance on English benchmarks for arbitrary-shaped text, achieving improvements of 0.7\% and 2.5\% on Total-Text and SCUT-CTW1500, respectively. These results validate our text spotter is effective for scene texts in arbitrary reading order and shape.

cs.CV↗

arXiv2024

Falcon-UI: Understanding GUI Before Following User Instructions

Pursuing human-like interaction for Graphical User Interface (GUI) agents requires understanding the GUI context and following user instructions. However, existing works typically couple these two aspects and focus more on instruct-following abilities, while ignoring the importance of understanding the GUI context. In this paper, we introduce an instruction-free GUI navigation dataset, termed Insight-UI Dataset, to enhance model comprehension of GUI environments. Insight-UI Dataset is automatically generated from the Common Crawl corpus, simulating various platforms -- including iOS, Android, Windows, and Linux -- across multiple resolutions on 312K domains. Although GUI interactions vary by context, diverse interfaces share common internal patterns, such as clicking an item to view its details. It implies the feasibility of independent GUI operation learning, followed by joint optimization with instruction tuning. Thereby, we develop the GUI agent model Falcon-UI, which is initially pretrained on Insight-UI Dataset and subsequently fine-tuned on Android and Web GUI datasets, including AITW, AITZ, Android Control, and Mind2Web. With 7 billion parameters, Falcon-UI achieves accuracy comparable to the 72 billion-parameter Qwen2VL on AITZ, validating the alignment between GUI context comprehension and agent performance. Our code and dataset will be open-sourced.

cs.CL↗

arXiv2024

A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor Segmentation

Brain tumor segmentation models have aided diagnosis in recent years. However, they face MRI complexity and variability challenges, including irregular shapes and unclear boundaries, leading to noise, misclassification, and incomplete segmentation, thereby limiting accuracy. To address these issues, we adhere to an outstanding Convolutional Neural Networks (CNNs) design paradigm and propose a novel network named A4-Unet. In A4-Unet, Deformable Large Kernel Attention (DLKA) is incorporated in the encoder, allowing for improved capture of multi-scale tumors. Swin Spatial Pyramid Pooling (SSPP) with cross-channel attention is employed in a bottleneck further to study long-distance dependencies within images and channel relationships. To enhance accuracy, a Combined Attention Module (CAM) with Discrete Cosine Transform (DCT) orthogonality for channel weighting and convolutional element-wise multiplication is introduced for spatial weighting in the decoder. Attention gates (AG) are added in the skip connection to highlight the foreground while suppressing irrelevant background information. The proposed network is evaluated on three authoritative MRI brain tumor benchmarks and a proprietary dataset, and it achieves a 94.4% Dice score on the BraTS 2020 dataset, thereby establishing multiple new state-of-the-art benchmarks. The code is available here: https://github.com/WendyWAAAAANG/A4-Unet.

cs.CV↗

arXiv2024

Unlocking Discovery Potential for Decaying Dark Matter and Faint X-ray Sources with XRISM

Astrophysical emission lines arising from particle decays can offer unique insights into the nature of dark matter (DM). Using dedicated simulations with background and foreground modeling, we comprehensively demonstrate that the recently launched XRISM space telescope with powerful X-ray spectroscopy capabilities is particularly well-suited to probe decaying DM, such as sterile neutrinos and axion-like particles, in the mass range of few to tens of keV. We analyze and map XRISM's DM discovery potential parameter space by considering Milky Way Galactic DM halo, including establishing an optimal line-of-sight search, as well as dwarf galaxies where we identify Segue 1 as a remarkably promising target. We demonstrate that with only 100 ks exposure XRISM/Resolve instrument is capable of probing the underexplored DM parameter window around few keV and testing DM couplings with sensitivity that exceeds by two orders existing Segue 1 limits. Further, we demonstrate that XRISM/Xtend instrument sensitivity enables discovery of the nature of faint astrophysical X-ray sources, especially in Segue 1, which could shed light on star-formation history. We discuss implications for decaying DM searches with improved detector energy resolution in future experiments.

astro-ph.HE↗

arXiv2024

Room-Temperature entangled quantum processor on integrated semiconductor photonics platform

The rise of the 4H-silicon-carbide-on-insulator (SiCOI) platform marks a promising pathway towards the realization of monolithic quantum photonic networks. However, the challenge of establishing room-temperature entangled registers on these integrated photonics platforms remains unresolved. Herein, we demonstrate the first entangled processor on the SiCOI platform. We show that both deterministic generation of single divacancy electron spins and near-unity spin initialization of a single $^{13}$C nuclear spin can be achieved on SiCOI at room temperature. Besides coherently manipulating the single nuclear spin, a maximally entangled state with a fidelity of 0.89 has been prepared on this CMOS-compatible semiconductor-integrated photonics system. This work establishes the foundation for compact and on-chip solutions within existing defect-based computing and sensing protocols, positioning the SiCOI platform as the most promising candidate for integrated monolithic quantum photonic networks.

quant-ph↗

arXiv2024

M3D: Dual-Stream Selective State Spaces and Depth-Driven Framework for High-Fidelity Single-View 3D Reconstruction

The precise reconstruction of 3D objects from a single RGB image in complex scenes presents a critical challenge in virtual reality, autonomous driving, and robotics. Existing neural implicit 3D representation methods face significant difficulties in balancing the extraction of global and local features, particularly in diverse and complex environments, leading to insufficient reconstruction precision and quality. We propose M3D, a novel single-view 3D reconstruction framework, to tackle these challenges. This framework adopts a dual-stream feature extraction strategy based on Selective State Spaces to effectively balance the extraction of global and local features, thereby improving scene comprehension and representation precision. Additionally, a parallel branch extracts depth information, effectively integrating visual and geometric features to enhance reconstruction quality and preserve intricate details. Experimental results indicate that the fusion of multi-scale features with depth information via the dual-branch feature extraction significantly boosts geometric consistency and fidelity, achieving state-of-the-art reconstruction performance.

cs.CV↗

arXiv2024

Anchor Attention, Small Cache: Code Generation with Large Language Models

The development of large language models (LLMs) has revolutionized automated code generation. However, their high demand of computation resources has hindered a broader deployment and raised environmental concerns. A common strategy for diminishing computational demands is to cache Key-Value (KV) states from the attention mechanism which is adopted predominately by mainstream LLMs. It can mitigate the need of repeated attention computations, but brings significant memory overhead. Current practices in NLP often use sparse attention which may, unfortunately, lead to substantial inaccuracies, or hallucinations, in code generation tasks. In this paper, we analyze the attention weights distribution within code generation models via an empirical study, uncovering a sparsity pattern, i.e., the aggregation of information at specific anchor points. Based on this observation, we propose a novel approach, AnchorCoder, which features token-wise anchor attention designed to extract and compress the contextual information, and layer-wise anchor attention enabling cross-layer communication to mitigate the issue of excessive superposition caused by the compression. The extensive experiments across multiple benchmark datasets confirm the effectiveness of AnchorCoder, which can consistently achieve a significant (at least 70%) reduction in KV cache requirements, while preserving the majority of model's performance.

cs.SE↗

arXiv2024

Memory Remedy: An AI-Enhanced Interactive Story Exploring Human-Robot Interaction and Companionship

We present our approach to using AI-generated content (AIGC) and multiple media to develop an immersive, game-based, interactive story experience. The narrative of the story, "Memory Remedy", unfolds through flashbacks, allowing the audience to gradually uncover the story and the complex relationship between the robot protagonist and the older adults. This exploration explores important themes such as the journey of life, the profound influence of memories, and the concept of post-human emotional care. By engaging with this AIGC-based interactive story, audiences are encouraged to reflect on the potential role of robotic companionship in the lives of older adults in the future; and to encourage deeper reflection on the complex relationship between artificial intelligence and humanity.

cs.HC↗

arXiv2024

Effective Action and Gravitational Pair Production in (A)dS Spacetime

We compute the effective action for a massive scalar field in (A)dS spacetime using the Euclidean heat kernel method. We highlight that in even-dimensional dS spacetimes, the effective action exhibits a non-trivial imaginary part, reminiscent of the Schwinger effect in quantum electrodynamics. We find consistency between the results obtained from the Euclidean heat kernel method with those from the Green's function approach in Lorentzian signature. Additionally, we compare our results with the perturbative calculations and find that the perturbation theory almost fails to capture the correct non-perturbative imaginary part of the effective action. This discrepancy presents a challenge to computing the gravitational pair production using the perturbation theory.

gr-qc↗

arXiv2024

TextCtrl: Diffusion-based Scene Text Editing with Prior Guidance Control

Centred on content modification and style preservation, Scene Text Editing (STE) remains a challenging task despite considerable progress in text-to-image synthesis and text-driven image manipulation recently. GAN-based STE methods generally encounter a common issue of model generalization, while Diffusion-based STE methods suffer from undesired style deviations. To address these problems, we propose TextCtrl, a diffusion-based method that edits text with prior guidance control. Our method consists of two key components: (i) By constructing fine-grained text style disentanglement and robust text glyph structure representation, TextCtrl explicitly incorporates Style-Structure guidance into model design and network training, significantly improving text style consistency and rendering accuracy. (ii) To further leverage the style prior, a Glyph-adaptive Mutual Self-attention mechanism is proposed which deconstructs the implicit fine-grained features of the source image to enhance style consistency and vision quality during inference. Furthermore, to fill the vacancy of the real-world STE evaluation benchmark, we create the first real-world image-pair dataset termed ScenePair for fair comparisons. Experiments demonstrate the effectiveness of TextCtrl compared with previous methods concerning both style fidelity and text accuracy.

cs.CV↗

arXiv2024

First Creating Backgrounds Then Rendering Texts: A New Paradigm for Visual Text Blending

Diffusion models, known for their impressive image generation abilities, have played a pivotal role in the rise of visual text generation. Nevertheless, existing visual text generation methods often focus on generating entire images with text prompts, leading to imprecise control and limited practicality. A more promising direction is visual text blending, which focuses on seamlessly merging texts onto text-free backgrounds. However, existing visual text blending methods often struggle to generate high-fidelity and diverse images due to a shortage of backgrounds for synthesis and limited generalization capabilities. To overcome these challenges, we propose a new visual text blending paradigm including both creating backgrounds and rendering texts. Specifically, a background generator is developed to produce high-fidelity and text-free natural images. Moreover, a text renderer named GlyphOnly is designed for achieving visually plausible text-background integration. GlyphOnly, built on a Stable Diffusion framework, utilizes glyphs and backgrounds as conditions for accurate rendering and consistency control, as well as equipped with an adaptive text block exploration strategy for small-scale text rendering. We also explore several downstream applications based on our method, including scene text dataset synthesis for boosting scene text detectors, as well as text image customization and editing. Code and model will be available at \url{https://github.com/Zhenhang-Li/GlyphOnly}.

cs.CV↗

arXiv2024

HM3: Hierarchical Multi-Objective Model Merging for Pretrained Models

Model merging is a technique that combines multiple large pretrained models into a single model with enhanced performance and broader task adaptability. It has gained popularity in large pretrained model development due to its ability to bypass the need for original training data and further training processes. However, most existing model merging approaches focus solely on exploring the parameter space, merging models with identical architectures. Merging within the architecture space, despite its potential, remains in its early stages due to the vast search space and the challenges of layer compatibility. This paper marks a significant advance toward more flexible and comprehensive model merging techniques by modeling the architecture-space merging process as a reinforcement learning task. We train policy and value networks using offline sampling of weight vectors, which are then employed for the online optimization of merging strategies. Moreover, a multi-objective optimization paradigm is introduced to accommodate users' diverse task preferences, learning the Pareto front of optimal models to offer customized merging suggestions. Experimental results across multiple tasks, including text translation, mathematical reasoning, and code generation, validate the effectiveness and superiority of the proposed framework in model merging. The code will be made publicly available after the review process.

cs.LG↗

arXiv2024

Quantum optical coherence theory based on Feynman's path integral

Compared to classical optical coherence theory based on Maxwell's electromagnetic theory and Glauber's quantum optical coherence theory based on matrix mechanics formulation of quantum mechanics, quantum optical coherence theory based on Feynman's path integral formulation of quantum mechanics provides a novel tool to study optical coherence. It has the advantage of understanding the connection between mathematical calculations and physical interpretations better. Quantum optical coherence theory based on Feynman's path integral is introduced and reviewed in this paper. Based on the results of transient first-order interference of two independent light beams, it is predicted that the classical model for electric field of thermal light introduced by classical optical textbooks may not be accurate. The physics of two-photon bunching of thermal light and Hong-Ou-Mandel dip of entangled photon pairs is the same, which can be interpreted by constructive and destructive two-photon interference, respectively. Quantum optical coherence theory based on Feynman's path integral is helpful to understand the coherence properties of light, which may eventually lead us to the answer of the question: what is a photon?

quant-ph↗

arXiv2024

Study of the relativistic charged particle beam propagation in Earth's magnetic field

Relativistic charged particle beam can be used as destructive beam weapons in space for debris removal tasks. The trajectories of charged particles are affected by both electric and magnetic forces in the Earth's magnetic field. In this paper, we firstly analyzed the correlation parameters of the charged particle beam as a weapon when it propagated in the geomagnetic field. Then the models were constructed based on COMSOL Multiphysics and the IGRF model was adopted in the simulation. The gyro-radius and the related uncertainty were analyzed by simulation of the charged particle transport in the geomagnetic field at different altitudes. The charged beam spot radius divergency was also simulated. The magnetic field pinch effect can be found and can limit the beam spreading.

physics.space-ph↗