arXiv subjects

Li Zhou

Publications and source records attributed to Li Zhou.

At least 73 records · Page 4Linked to original sources

arXiv2025

Meta-DSP: A Meta-Learning Approach for Data-Driven Nonlinear Compensation in High-Speed Optical Fiber Systems

Nonlinear effects in high-speed optical fiber systems fundamentally limit channel capacity. While traditional Digital Backward Propagation (DBP) with adaptive filters addresses these effects, its computational complexity remains impractical. Data-driven solutions like Filtered DBP (FDBP) reduce complexity but critically lack inherent generalization: Their nonlinear compensation capability cannot be naturally extended to new transmission rates or WDM channel counts without retraining on newly collected data. We propose Meta-DSP, a novel signal processing pipeline combining: (1) Meta-DBP, a meta-learning-based DBP model that generalizes across transmission parameters without retraining, and (2) XPM-ADF, a carefully engineered adaptive filter designed to address multi-channel nonlinear distortions. The system demonstrates strong generalization, learning from 40 Gbaud single-channel data and successfully applying this knowledge to higher rates (80/160 Gbaud) and multi-channel configurations (up to 21 channels). Experimental results show Meta-DSP improves Q-factor by 0.55 dB over CDC in challenging scenarios while reducing computational complexity 10$\times$ versus DBP. This work provides a scalable solution for nonlinear compensation in dynamic optical networks, balancing performance with practical computational constraints.

eess.SP↗

arXiv2025

ResPF: Residual Poisson Flow for Efficient and Physically Consistent Sparse-View CT Reconstruction

Sparse-view computed tomography (CT) is a practical solution to reduce radiation dose, but the resulting ill-posed inverse problem poses significant challenges for accurate image reconstruction. Although deep learning and diffusion-based methods have shown promising results, they often lack physical interpretability or suffer from high computational costs due to iterative sampling starting from random noise. Recent advances in generative modeling, particularly Poisson Flow Generative Models (PFGM), enable high-fidelity image synthesis by modeling the full data distribution. In this work, we propose Residual Poisson Flow (ResPF) Generative Models for efficient and accurate sparse-view CT reconstruction. Based on PFGM++, ResPF integrates conditional guidance from sparse measurements and employs a hijacking strategy to significantly reduce sampling cost by skipping redundant initial steps. However, skipping early stages can degrade reconstruction quality and introduce unrealistic structures. To address this, we embed a data-consistency into each iteration, ensuring fidelity to sparse-view measurements. Yet, PFGM sampling relies on a fixed ordinary differential equation (ODE) trajectory induced by electrostatic fields, which can be disrupted by step-wise data consistency, resulting in unstable or degraded reconstructions. Inspired by ResNet, we introduce a residual fusion module to linearly combine generative outputs with data-consistent reconstructions, effectively preserving trajectory continuity. To the best of our knowledge, this is the first application of Poisson flow models to sparse-view CT. Extensive experiments on synthetic and clinical datasets demonstrate that ResPF achieves superior reconstruction quality, faster inference, and stronger robustness compared to state-of-the-art iterative, learning-based, and diffusion models.

eess.IV↗

arXiv2025

Selective Structured State Space for Multispectral-fused Small Target Detection

Target detection in high-resolution remote sensing imagery faces challenges due to the low recognition accuracy of small targets and high computational costs. The computational complexity of the Transformer architecture increases quadratically with image resolution, while Convolutional Neural Networks (CNN) architectures are forced to stack deeper convolutional layers to expand their receptive fields, leading to an explosive growth in computational demands. To address these computational constraints, we leverage Mamba's linear complexity for efficiency. However, Mamba's performance declines for small targets, primarily because small targets occupy a limited area in the image and have limited semantic information. Accurate identification of these small targets necessitates not only Mamba's global attention capabilities but also the precise capture of fine local details. To this end, we enhance Mamba by developing the Enhanced Small Target Detection (ESTD) module and the Convolutional Attention Residual Gate (CARG) module. The ESTD module bolsters local attention to capture fine-grained details, while the CARG module, built upon Mamba, emphasizes spatial and channel-wise information, collectively improving the model's ability to capture distinctive representations of small targets. Additionally, to highlight the semantic representation of small targets, we design a Mask Enhanced Pixel-level Fusion (MEPF) module for multispectral fusion, which enhances target features by effectively fusing visible and infrared multimodal information.

cs.CV↗

arXiv2025

HST/WFC3 Constraints on the Abundances of OH and FeH in the Atmosphere of the Ultra-Hot Neptune LTT-9779 b

Planets residing within the hot-Neptune Desert are rare, and studying their atmospheres can provide valuable insights into their formation and evolutionary processes. We present the atmospheric characterization of the first known ultra-hot Neptune, LTT-9779 b, using transmission spectroscopic observations obtained with the HST/WFC3 G141 and G102 grisms. Using the Iraclis pipeline and TauREx3 retrieval code, we find that LTT-9779 b likely possesses a H/He-dominated primary atmosphere with an opaque aerosol layer and the pure cloudy, flat-line model is rejected with approximately 2.7-$σ$ confidence. Although we do not find conclusive evidence supporting the presence of any molecular species, we place 95% confidence level upper limits on the volume mixing ratios (VMRs) of hydroxyl radical (OH) and iron hydride (FeH) at $7.18\times10^{-2}$ and $1.52\times10^{-8}$, respectively. Notably, the retrieval results are inconsistent with predictions from equilibrium chemistry models, which favor higher $\rm H_2O$ abundances over OH. This discrepancy suggests that disequilibrium processes, such as photochemistry or vertical mixing, may have altered the atmospheric composition. Comparisons between HST, Spitzer and JWST data reveal no evidence of temporal variations in the atmospheric composition of the terminator region. Our results highlight the need for higher-resolution spectroscopy and secondary eclipse observations to resolve LTT-9779 b's temperature-pressure (T-P) profile and chemical inventory definitively.

astro-ph.EP↗

arXiv2025

D-Hammer: Efficient Equational Reasoning for Labelled Dirac Notation

Labelled Dirac notation is a formalism commonly used by physicists to represent many-body quantum systems and by computer scientists to assert properties of quantum programs. It is supported by a rich equational theory for proving equality between expressions in the language. These proofs are typically carried on pen-and-paper, and can be exceedingly long and error-prone. We introduce D-Hammer, the first tool to support automated equational proof for labelled Dirac notation. The salient features of D-Hammer include: an expressive, higher-order, dependently-typed language for labelled Dirac notation; an efficient normalization algorithm; and an optimized C++ implementation. We evaluate the implementation on representative examples from both plain and labelled Dirac notation. In the case of plain Dirac notation, we show that our implementation significantly outperforms DiracDec.

cs.PL↗

arXiv2025

ReGraph: A Tool for Binary Similarity Identification

Binary Code Similarity Detection (BCSD) is not only essential for security tasks such as vulnerability identification but also for code copying detection, yet it remains challenging due to binary stripping and diverse compilation environments. Existing methods tend to adopt increasingly complex neural networks for better accuracy performance. The computation time increases with the complexity. Even with powerful GPUs, the treatment of large-scale software becomes time-consuming. To address these issues, we present a framework called ReGraph to efficiently compare binary code functions across architectures and optimization levels. Our evaluation with public datasets highlights that ReGraph exhibits a significant speed advantage, performing 700 times faster than Natural Language Processing (NLP)-based methods while maintaining comparable accuracy results with respect to the state-of-the-art models.

cs.CR↗

arXiv2025

Infrared Emission of Polycyclic Aromatic Hydrocarbon Molecules in Titan: Cyanonaphthalenes

As the only moon in the solar system with a thick atmosphere, Titan is a compelling and enigmatic world containing a complex organic haze. Polycyclic aromatic hydrocarbon (PAH) molecules are believed to play an essential role in the formation of Titan's aerosols and haze layers. The existence of PAHs in Titan's upper atmosphere has been revealed by the detection of the 3.28-micron emission band with Cassini's Visual and Infrared Mapping Spectrometer (VIMS). However, there is little knowledge about the identity, composition, size and abundance of PAH molecules in Titan's atmosphere. Due to its unprecedented sensitivity and spectral coverage and resolution, the advent of the James Webb Space Telescope (JWST) could possibly enable a full characterization of the chemical makeups of Titan's aerosols. In particular, with a much better spectral resolution than Cassini's VIMS, JWST's Near Infrared Spectrograph (and Mid Infrared Instrument) could enable the spectral bands to be better resolved, potentially providing crucial information about which PAHs are really present in Titan's upper atmosphere. To facilitate JWST to search for and identify Titan's PAH molecules, we are performing a systematic study of the photophysics of PAHs in Titan's upper atmosphere. As a pilot study, here we report the infrared emission spectra of vibrationally excited cyanonapthalenes and their ions which are composed of two fused benzene rings and one nitrile (-CN) group. The calculated emission spectra will help JWST to quantitatively determine or place an upper limit on the abundances of cyanonapthalenes in Titan's upper atmosphere.

astro-ph.EP↗

arXiv2025

Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning

In federated learning, client selection is a critical problem that significantly impacts both model performance and fairness. Prior studies typically treat these two objectives separately, or balance them using simple weighting schemes. However, we observe that commonly used metrics for model performance and fairness often conflict with each other, and a straightforward weighted combination is insufficient to capture their complex interactions. To address this, we first propose two guiding principles that directly tackle the inherent conflict between the two metrics while reinforcing each other. Based on these principles, we formulate the client selection problem as a long-term optimization task, leveraging the Lyapunov function and the submodular nature of the problem to solve it effectively. Experiments show that the proposed method improves both model performance and fairness, guiding the system to converge comparably to full client participation. This improvement can be attributed to the fact that both model performance and fairness benefit from the diversity of the selected clients' data distributions. Our approach adaptively enhances this diversity by selecting clients based on their data distributions, thereby improving both model performance and fairness.

cs.LG↗

arXiv2025

Formalization of Optimality Conditions for Smooth Constrained Optimization Problems

Optimality conditions are central to analysis of optimization problems, characterizing necessary criteria for local minima. Formalizing the optimality conditions within the type-theory-based proof assistant Lean4 provides a precise, robust, and reusable framework essential for rigorous verification in optimization theory. In this paper, we introduce a formalization of the first-order optimality conditions (also known as the Karush-Kuhn-Tucker (KKT) conditions) for smooth constrained optimization problems by beginning with concepts such as the Lagrangian function and constraint qualifications. The geometric optimality conditions are then formalized, offering insights into local minima through tangent cones. We also establish the critical equivalence between the tangent cone and linearized feasible directions under appropriate constraint qualifications. Building on these key elements, the formalization concludes the KKT conditions through the proof of the Farkas lemma. Additionally, this study provides a formalization of the dual problem and the weak duality property.

math.OC↗

arXiv2025

Absence of dehydration due to superionic transition at Earth's core-mantle boundary

The properties and stability of hydrous phases are key to unraveling the mysteries of the water cycle in Earth's interior. Under the deep lower mantle conditions, hydrous phases transition into a superionic state. However, the influence of the superionic effect on their stability and dehydration processes remains poorly understood. Using ab initio calculations and deep-learning potential molecular dynamics simulations, we discovered a doubly superionic transition in delta-AlOOH, characterized by the highly diffusive behavior of ionic hydrogen and aluminum within the oxygen sub-lattice. These highly diffusive elements contribute significant external entropy into the system, resulting in exceptional thermostability. Free energy calculations indicate that dehydration is energetically and kinetically unfavorable when water exists in a superionic state under core-mantle boundary (CMB) conditions. Consequently, water can accumulate in the deep lower mantle over Earth's history. This deep water reservoir plays a crucial role in the global deep water and hydrogen cycles.

physics.geo-ph↗

arXiv2025

Physics-informed Score-based Diffusion Model for Limited-angle Reconstruction of Cardiac Computed Tomography

Cardiac computed tomography (CT) has emerged as a major imaging modality for the diagnosis and monitoring of cardiovascular diseases. High temporal resolution is essential to ensure diagnostic accuracy. Limited-angle data acquisition can reduce scan time and improve temporal resolution, but typically leads to severe image degradation and motivates for improved reconstruction techniques. In this paper, we propose a novel physics-informed score-based diffusion model (PSDM) for limited-angle reconstruction of cardiac CT. At the sampling time, we combine a data prior from a diffusion model and a model prior obtained via an iterative algorithm and Fourier fusion to further enhance the image quality. Specifically, our approach integrates the primal-dual hybrid gradient (PDHG) algorithm with score-based diffusion models, thereby enabling us to reconstruct high-quality cardiac CT images from limited-angle data. The numerical simulations and real data experiments confirm the effectiveness of our proposed approach.

eess.IV↗

arXiv2025

Step-Audio: Unified Understanding and Generation in Intelligent Speech Interaction

Real-time speech interaction, serving as a fundamental interface for human-machine collaboration, holds immense potential. However, current open-source models face limitations such as high costs in voice data collection, weakness in dynamic control, and limited intelligence. To address these challenges, this paper introduces Step-Audio, the first production-ready open-source solution. Key contributions include: 1) a 130B-parameter unified speech-text multi-modal model that achieves unified understanding and generation, with the Step-Audio-Chat version open-sourced; 2) a generative speech data engine that establishes an affordable voice cloning framework and produces the open-sourced lightweight Step-Audio-TTS-3B model through distillation; 3) an instruction-driven fine control system enabling dynamic adjustments across dialects, emotions, singing, and RAP; 4) an enhanced cognitive architecture augmented with tool calling and role-playing abilities to manage complex tasks effectively. Based on our new StepEval-Audio-360 evaluation benchmark, Step-Audio achieves state-of-the-art performance in human evaluations, especially in terms of instruction following. On open-source benchmarks like LLaMA Question, shows 9.3% average performance improvement, demonstrating our commitment to advancing the development of open-source multi-modal language technologies. Our code and models are available at https://github.com/stepfun-ai/Step-Audio.

cs.CL↗

arXiv2025

Large Language Models Penetration in Scholarly Writing and Peer Review

While the widespread use of Large Language Models (LLMs) brings convenience, it also raises concerns about the credibility of academic research and scholarly processes. To better understand these dynamics, we evaluate the penetration of LLMs across academic workflows from multiple perspectives and dimensions, providing compelling evidence of their growing influence. We propose a framework with two components: \texttt{ScholarLens}, a curated dataset of human- and LLM-generated content across scholarly writing and peer review for multi-perspective evaluation, and \texttt{LLMetrica}, a tool for assessing LLM penetration using rule-based metrics and model-based detectors for multi-dimensional evaluation. Our experiments demonstrate the effectiveness of \texttt{LLMetrica}, revealing the increasing role of LLMs in scholarly processes. These findings emphasize the need for transparency, accountability, and ethical practices in LLM usage to maintain academic credibility.

cs.CL↗

arXiv2025

Does Mapo Tofu Contain Coffee? Probing LLMs for Food-related Cultural Knowledge

Recent studies have highlighted the presence of cultural biases in Large Language Models (LLMs), yet often lack a robust methodology to dissect these phenomena comprehensively. Our work aims to bridge this gap by delving into the Food domain, a universally relevant yet culturally diverse aspect of human life. We introduce FmLAMA, a multilingual dataset centered on food-related cultural facts and variations in food practices. We analyze LLMs across various architectures and configurations, evaluating their performance in both monolingual and multilingual settings. By leveraging templates in six different languages, we investigate how LLMs interact with language-specific and cultural knowledge. Our findings reveal that (1) LLMs demonstrate a pronounced bias towards food knowledge prevalent in the United States; (2) Incorporating relevant cultural context significantly improves LLMs' ability to access cultural knowledge; (3) The efficacy of LLMs in capturing cultural nuances is highly dependent on the interplay between the probing language, the specific model architecture, and the cultural context in question. This research underscores the complexity of integrating cultural understanding into LLMs and emphasizes the importance of culturally diverse datasets to mitigate biases and enhance model performance across different cultural domains.

cs.CL↗

arXiv2025

Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement

The rapid development of large language models (LLMs), like ChatGPT, has resulted in the widespread presence of LLM-generated content on social media platforms, raising concerns about misinformation, data biases, and privacy violations, which can undermine trust in online discourse. While detecting LLM-generated content is crucial for mitigating these risks, current methods often focus on binary classification, failing to address the complexities of real-world scenarios like human-LLM collaboration. To move beyond binary classification and address these challenges, we propose a new paradigm for detecting LLM-generated content. This approach introduces two novel tasks: LLM Role Recognition (LLM-RR), a multi-class classification task that identifies specific roles of LLM in content generation, and LLM Influence Measurement (LLM-IM), a regression task that quantifies the extent of LLM involvement in content creation. To support these tasks, we propose LLMDetect, a benchmark designed to evaluate detectors' performance on these new tasks. LLMDetect includes the Hybrid News Detection Corpus (HNDC) for training detectors, as well as DetectEval, a comprehensive evaluation suite that considers five distinct cross-context variations and two multi-intensity variations within the same LLM role. This allows for a thorough assessment of detectors' generalization and robustness across diverse contexts. Our empirical validation of 10 baseline detection methods demonstrates that fine-tuned PLM-based models consistently outperform others on both tasks, while advanced LLMs face challenges in accurately detecting their own generated content. Our experimental results and analysis offer insights for developing more effective detection models for LLM-generated content. This research enhances the understanding of LLM-generated content and establishes a foundation for more nuanced detection methodologies.

cs.CL↗

arXiv2025

Complete Quantum Relational Hoare Logics from Optimal Transport Duality

We introduce a quantitative relational Hoare logic for quantum programs. Assertions of the logic range over a new infinitary extension of positive semidefinite operators. We prove that our logic is sound, and complete for bounded postconditions and almost surely terminating programs. Our completeness result is based on a quantum version of the duality theorem from optimal transport. We also define a complete embedding into our logic of a relational Hoare logic with projective assertions.

cs.LO↗

arXiv2025

Exoplanet Ephemerides Change Observations (ExoEcho). I. Transit Timing Analysis of Thirty-Seven Exoplanets using HST/WFC3 Data

The ExoEcho project is designed to study the photodynamics of exoplanets by leveraging high-precision transit timing data from ground- and space-based telescopes. Some exoplanets are experiencing orbital decay, and transit timing variation (TTV) is a useful technique to study their orbital period variations. In this study, we have obtained transit middle-time data from the Hubble Space Telescope (HST) observations for 37 short-period exoplanets, most of which are hot Jupiters. To search for potential long- and short-term orbital period variations within the sample, we conduct TTV model fitting using both linear and quadratic ephemeris models. Our analysis identifies two hot Jupiters experiencing strong periodic decays. Given the old age of the host stars of the hot Jupiter population, our findings call for a scenario where HJs are continuously being destructed and created. Our study demonstrates the importance of incorporating high-precision transit timing data to TTV study in the future.

astro-ph.EP↗

arXiv2024

RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions

Retrieval-Augmented Generation (RAG) has emerged as a key paradigm for enhancing large language models (LLMs) by incorporating external knowledge. However, current RAG methods face two limitations: (1) they only cover limited RAG scenarios. (2) They suffer from limited task diversity due to the lack of a general RAG dataset. To address these limitations, we propose RAG-Instruct, a general method for synthesizing diverse and high-quality RAG instruction data based on any source corpus. Our approach leverages (1) five RAG paradigms, which encompass diverse query-document relationships, and (2) instruction simulation, which enhances instruction diversity and quality by utilizing the strengths of existing instruction datasets. Using this method, we construct a 40K instruction dataset from Wikipedia, comprehensively covering diverse RAG scenarios and tasks. Experiments demonstrate that RAG-Instruct effectively enhances LLMs' RAG capabilities, achieving strong zero-shot performance and significantly outperforming various RAG baselines across a diverse set of tasks. RAG-Instruct is publicly available at https://github.com/FreedomIntelligence/RAG-Instruct.

cs.CL↗