arXiv subjects

Fei Ding

Publications and source records attributed to Fei Ding.

At least 55 records · Page 3Linked to original sources

arXiv2025

Dynamic and Continuous Control of Second-Harmonic Chirality through Lithium Niobate Nonlocal Metasurface

Nonlinear chiral light sources are crucial for emerging applications in chiroptics, including ultrafast spin dynamics and quantum state manipulation. However, achieving precise and dynamic control over nonlinear optical chirality with natural materials and metasurfaces, particularly those based on non-centrosymmetric materials such as lithium niobate (LN), is hindered by the complex tensorial nature of the second-order nonlinear susceptibility. Here, we demonstrate a nonlinear nonlocal metasurface, comprising plasmonic nanoantennas atop an x-cut LN thin film, that enables dynamic and continuous control of second-harmonic (SH) chirality. By leveraging two spectrally detuned resonances arising from the excitation of orthogonally propagating guided modes, enabled by lattice anisotropy and LN birefringence, we achieve full-range tuning of SH chirality, from right- to left-handed circular polarization, simply by rotating the polarization of a linearly polarized pump. The SH chirality, quantified by the Stokes parameter S3, is thereby continuously tuned from 0.991 to -0.993 in simulations and from 0.920 to -0.815 in experiments, while maintaining consistently enhanced SH intensity across the entire tuning range. Our approach opens new avenues for developing compact and tunable chiral sources, with potential applications in integrated nonlinear photonics and adaptable quantum technologies.

physics.optics↗

arXiv2025

The enhanced X-ray Timing and Polarimetry mission -- eXTP for launch in 2030

In this paper we present the current status of the enhanced X-ray Timing and Polarimetry mission, which has been fully approved for launch in 2030. eXTP is a space science mission designed to study fundamental physics under extreme conditions of matter density, gravity, and magnetism. The mission aims at determining the equation of state of matter at supra-nuclear density, measuring the effects of quantum electro-dynamics, and understanding the dynamics of matter in strong-field gravity. In addition to investigating fundamental physics, the eXTP mission is poised to become a leading observatory for time-domain and multi-messenger astronomy in the 2030's, as well as providing observations of unprecedented quality on a variety of galactic and extragalactic objects. After briefly introducing the history and a summary of the scientific objectives of the eXTP mission, this paper presents a comprehensive overview of: 1) the cutting-edge technology, technical specifications, and anticipated performance of the mission's scientific instruments; 2) the full mission profile, encompassing spacecraft design, operational capabilities, and ground segment infrastructure.

astro-ph.HE↗

arXiv2025

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward

Recent advancements in image customization exhibit a wide range of application prospects due to stronger customization capabilities. However, since we humans are more sensitive to faces, a significant challenge remains in preserving consistent identity while avoiding identity confusion with multi-reference images, limiting the identity scalability of customization models. To address this, we present UMO, a Unified Multi-identity Optimization framework, designed to maintain high-fidelity identity preservation and alleviate identity confusion with scalability. With "multi-to-multi matching" paradigm, UMO reformulates multi-identity generation as a global assignment optimization problem and unleashes multi-identity consistency for existing image customization methods generally through reinforcement learning on diffusion models. To facilitate the training of UMO, we develop a scalable customization dataset with multi-reference images, consisting of both synthesised and real parts. Additionally, we propose a new metric to measure identity confusion. Extensive experiments demonstrate that UMO not only improves identity consistency significantly, but also reduces identity confusion on several image customization methods, setting a new state-of-the-art among open-source methods along the dimension of identity preserving. Code and model: https://github.com/bytedance/UMO

cs.CV↗

arXiv2025

USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

Existing literature typically treats style-driven and subject-driven generation as two disjoint tasks: the former prioritizes stylistic similarity, whereas the latter insists on subject consistency, resulting in an apparent antagonism. We argue that both objectives can be unified under a single framework because they ultimately concern the disentanglement and re-composition of content and style, a long-standing theme in style-driven research. To this end, we present USO, a Unified Style-Subject Optimized customization model. First, we construct a large-scale triplet dataset consisting of content images, style images, and their corresponding stylized content images. Second, we introduce a disentangled learning scheme that simultaneously aligns style features and disentangles content from style through two complementary objectives, style-alignment training and content-style disentanglement training. Third, we incorporate a style reward-learning paradigm denoted as SRL to further enhance the model's performance. Finally, we release USO-Bench, the first benchmark that jointly evaluates style similarity and subject fidelity across multiple metrics. Extensive experiments demonstrate that USO achieves state-of-the-art performance among open-source models along both dimensions of subject consistency and style similarity. Code and model: https://github.com/bytedance/USO

cs.CV↗

arXiv2025

Deterministic Integration of CsPbBr3 Quantum Dots with Plasmonic Ring Microcavities

Perovskite quantum dots hold great promise for quantum information processing as wavelength-tunable single photon sources operable over a broad temperature range. However, their deterministic integration into nanophotonic structures remains a major challenge, limited by their random spatial distribution and non-directional emission. In this work, we employ a two-step electron beam lithography process to deterministically place CsPbBr3 quantum dots within the mode volume of plasmonic ring microcavities. Simulations predict strong field enhancement within the cavity, boosting photon emission rates via the Purcell effect and improving the quantum efficiency of the emitters. Experimentally, coupling ensembles of CsPbBr3 quantum dots to the cavities results in a four-fold enhancement in photoluminescence intensity and a three-fold reduction in fluorescence lifetime at room temperature. Single-emitter coupling is further investigated at cryogenic temperatures, leading to a two-fold reduction in radiative lifetime. These results demonstrate a scalable approach for the integration of perovskite quantum dots into nanophotonic cavities and quantum photonic circuits.

physics.optics↗

arXiv2025

Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning

Since the advent of autonomous driving technology, it has experienced remarkable progress over the last decade. However, most existing research still struggles to address the challenges posed by environments where multiple vehicles have to interact seamlessly. This study aims to integrate causal learning with reinforcement learning-based methods by leveraging causal disentanglement representation learning (CDRL) to identify and extract causal features that influence optimal decision-making in autonomous vehicles. These features are then incorporated into graph neural network-based reinforcement learning algorithms to enhance decision-making in complex traffic scenarios. By using causal features as inputs, the proposed approach enables the optimization of vehicle behavior at an unsignalized intersection. Experimental results demonstrate that our proposed method achieves the highest average reward during training and our approach significantly outperforms other learning-based methods in several key metrics such as collision rate and average cumulative reward during testing. This study provides a promising direction for advancing multi-agent autonomous driving systems and make autonomous vehicles' navigation safer and more efficient in complex traffic environments.

cs.MA↗

arXiv2025

Ultrastable, low-error dynamic polarization encoding of deterministically generated single photons

The ability to inscribe information on single photons at high speeds is a crucial requirement for quantum applications such as quantum communication and measurement-based photonic quantum computation. Nowadays, most experimental implementations employ phase modulators in single-pass, Mach-Zehnder interferometer or Michelson interferometer configurations to encode information on photonic qubits. However, these approaches are intrinsically sensitive to environmental influences, limiting the achievable quantum error rates in practice. We report on the first demonstration of a polarization encoder for single-photon qubits based on a free-space Sagnac interferometer, showcasing inherent phase stability and overcoming previous error rate limitations. Telecom-wavelength single photons emitted by a quantum dot are modulated by the encoder under a repetition rate of 152 MHz. A quantum bit error rate of 0.69(2)% is achieved, marking the lowest error rate reported to date for high-speed information encoding on single photons. This work represents a key advance towards robust, scalable, and low-error quantum information processing with single photon sources.

quant-ph↗

arXiv2025

Improved Supervised Fine-Tuning for Large Language Models to Mitigate Catastrophic Forgetting

Supervised Fine-Tuning (SFT) is a critical step for enhancing the instruction-following capabilities of Large Language Models (LLMs) and adapting them to specialized domains. However, SFT often leads to a degradation of the model's general abilities, a phenomenon known as catastrophic forgetting. This problem is exacerbated when third-party practitioners fine-tune open-source models, as the original SFT data is typically not available. To address this challenge, we propose a novel and cost-effective SFT method that effectively mitigates catastrophic forgetting without requiring access to the original SFT data. Our approach first reconstructs the likely instruction distribution of the base model. It then employs a multi-model generation and filtering pipeline to synthesize a high-quality general-purpose dataset. This synthetic dataset is mixed with new, domain-specific data for fine-tuning. Experimental results show that our method not only preserves the model's capabilities in general domains but also improves task-specific performance, outperforming baselines that use publicly available SFT datasets.

cs.CL↗

arXiv2025

Multi-Layer GRPO: Enhancing Reasoning and Self-Correction in Large Language Models

The Group Relative Policy Optimization (GRPO) algorithm has demonstrated considerable success in enhancing the reasoning capabilities of large language models (LLMs), as evidenced by DeepSeek-R1. However, the absence of intermediate supervision in GRPO frequently leads to inefficient exploration dynamics. A single error in a complex reasoning chain can invalidate the entire solution, resulting in abrupt reward vanishing and compromising training stability.To address these challenges, we propose MGRPO (Multi-layer GRPO). MGRPO operates in two layers: the first layer employs standard GRPO to generate an initial response. This response, along with the original query, is then fed into a second-layer GRPO process. This second layer is specifically trained to identify and correct errors in the initial response, effectively creating a self-correction loop. This mechanism provides implicit process-level supervision by rewarding successful error correction, without requiring an explicit, densely-annotated reward model. Experimental results on several mathematical reasoning benchmarks demonstrate that MGRPO significantly outperforms standard GRPO, achieving superior performance by fostering both reasoning and self-correction abilities.

cs.LG↗

arXiv2025

Socially-Aware Autonomous Driving: Inferring Yielding Intentions for Safer Interactions

Since the emergence of autonomous driving technology, it has advanced rapidly over the past decade. It is becoming increasingly likely that autonomous vehicles (AVs) would soon coexist with human-driven vehicles (HVs) on the roads. Currently, safety and reliable decision-making remain significant challenges, particularly when AVs are navigating lane changes and interacting with surrounding HVs. Therefore, precise estimation of the intentions of surrounding HVs can assist AVs in making more reliable and safe lane change decision-making. This involves not only understanding their current behaviors but also predicting their future motions without any direct communication. However, distinguishing between the passing and yielding intentions of surrounding HVs still remains ambiguous. To address the challenge, we propose a social intention estimation algorithm rooted in Directed Acyclic Graph (DAG), coupled with a decision-making framework employing Deep Reinforcement Learning (DRL) algorithms. To evaluate the method's performance, the proposed framework can be tested and applied in a lane-changing scenario within a simulated environment. Furthermore, the experiment results demonstrate how our approach enhances the ability of AVs to navigate lane changes safely and efficiently on roads.

cs.RO↗

arXiv2025

Less-to-More Generalization: Unlocking More Controllability by In-Context Generation

Although subject-driven generation has been extensively explored in image generation due to its wide applications, it still has challenges in data scalability and subject expansibility. For the first challenge, moving from curating single-subject datasets to multiple-subject ones and scaling them is particularly difficult. For the second, most recent methods center on single-subject generation, making it hard to apply when dealing with multi-subject scenarios. In this study, we propose a highly-consistent data synthesis pipeline to tackle this challenge. This pipeline harnesses the intrinsic in-context generation capabilities of diffusion transformers and generates high-consistency multi-subject paired data. Additionally, we introduce UNO, which consists of progressive cross-modal alignment and universal rotary position embedding. It is a multi-image conditioned subject-to-image model iteratively trained from a text-to-image model. Extensive experiments show that our method can achieve high consistency while ensuring controllability in both single-subject and multi-subject driven generation.

cs.CV↗

arXiv2025

Channelling Multimodality Through a Unimodalizing Transport: Warp-U Sampler and Stochastic Bridge Sampling Estimator

Monte Carlo integration is a powerful tool for scientific and statistical computation, but faces significant challenges when the integrand is a multi-modal distribution, even when the mode locations are known. This work introduces novel Monte Carlo sampling and integration estimation strategies for the multi-modal context by leveraging a generalized version of the stochastic Warp-U transformation Wang et al. [2022]. We propose two flexible classes of Warp-U transformations, one based on a general location-scale-skew mixture model and a second using neural ordinary differential equations. We develop an efficient sampling strategy called Warp-U sampling, which applies a Warp-U transformation to map a multi-modal density into a uni-modal one, then inverts the transformation with injected stochasticity. In high dimensions, our approach relies on information about the mode locations, but requires minimal tuning and demonstrates better mixing properties than conventional methods with identical mode information. To improve normalizing constant estimation once samples are obtained, we propose a stochastic Warp-U bridge sampling estimator, which we demonstrate has higher asymptotic precision per CPU second compared to the original approach proposed by Wang et al. [2022]. We also establish the ergodicity of our sampling algorithm. The effectiveness and current limitations of our methods are illustrated through simulation studies and an application to exoplanet detection.

stat.ME↗

arXiv2025

On-Chip Emitter-Coupled Meta-Optics for Versatile Photon Sources

Controlling the spontaneous emission of nanoscale quantum emitters (QEs) is crucial for developing advanced photon sources required in many areas of modern nanophotonics, including quantum information technologies. Conventional approaches to shaping photon emission are based on using bulky configurations, while approaches recently developed in quantum metaphotonics suffer from limited capabilities in achieving desired polarization states and directionality, failing to provide on-demand photon sources tailored precisely to technological needs. Here, we propose a universal approach to designing versatile photon sources using on-chip QE-coupled meta-optics that enable direct transformations of QE-excited surface plasmon polaritons into spatially propagating photon streams with arbitrary polarization states, directionality, and amplitudes via both resonance and geometric phases supplied by meta-atoms. Leveraging this platform, we experimentally demonstrate the independent engineering of polarization states, directionality, and relative amplitudes of photon emission in single and multiple radiation channels. The developed universal approach represents a major step towards the realization of versatile quantum sources, highlighting the potential of integrated photon sources with superior properties for both classical and quantum information technologies.

physics.optics↗

arXiv2025

Metasurface Polarimeter for Structural Imaging and Tissue Diagnostics

Histopathology, the study and diagnosis of disease through analysis of tissue samples, is an indispensable part of modern medicine. However, the practice is time consuming and labor intensive, compelling efforts to improve the process and develop new approaches. One perspective technique involves mapping changes in the polarization state of light scattered by the tissue, but the conventional implementation requires bulky polarization optics and is slow. We report the design, fabrication and characterization of a compact metasurface polarimeter operating at 640 nm enabling simultaneous determination of Stokes parameters and degree of polarization with $\pm$2% accuracy. To validate its use for histopathology we map polarization state changes in a tissue phantom mimicking a biopsy with a cancerous inclusion, comparing it to a commercial polarimeter. The results indicate a great potential and suggest several improvements with which we believe metasurface polarimeter based devices will be ready for practical histopathology application in clinical environment.

physics.optics↗

arXiv2024

VMix: Improving Text-to-Image Diffusion Model with Cross-Attention Mixing Control

While diffusion models show extraordinary talents in text-to-image generation, they may still fail to generate highly aesthetic images. More specifically, there is still a gap between the generated images and the real-world aesthetic images in finer-grained dimensions including color, lighting, composition, etc. In this paper, we propose Cross-Attention Value Mixing Control (VMix) Adapter, a plug-and-play aesthetics adapter, to upgrade the quality of generated images while maintaining generality across visual concepts by (1) disentangling the input text prompt into the content description and aesthetic description by the initialization of aesthetic embedding, and (2) integrating aesthetic conditions into the denoising process through value-mixed cross-attention, with the network connected by zero-initialized linear layers. Our key insight is to enhance the aesthetic presentation of existing diffusion models by designing a superior condition control method, all while preserving the image-text alignment. Through our meticulous design, VMix is flexible enough to be applied to community models for better visual performance without retraining. To validate the effectiveness of our method, we conducted extensive experiments, showing that VMix outperforms other state-of-the-art methods and is compatible with other community modules (e.g., LoRA, ControlNet, and IPAdapter) for image generation. The project page is https://vmix-diffusion.github.io/VMix/.

cs.CV↗

arXiv2024

DERs-Aided Blackstart and Load Restoration Framework for Distribution Systems Considering Synchronization and Frequency Security Constraints

Extreme weather events have led to long-duration outages in the distribution system (DS), necessitating novel approaches to blackstart and restore the system. Existing blackstart solutions utilize blackstart units to establish multiple microgrids, sequentially energize non-blackstart units, and restore loads. However, these approaches often result in isolated microgrids. In DER-aided blackstart, the continuous operation of these microgrids is uncertain due to the finite energy capacity of commonly used blackstart units, such as battery energy storage (BES)-based grid-forming inverters (GFMIs). To address this issue, this article proposes a holistic blackstart and restoration framework that incorporates synchronization between microgrids and the entire DS with the transmission grid (TG). To support synchronization, we leveraged virtual synchronous generator-based control for GFMIs to estimate their frequency response to load pick-up events using only initial/final quasi-steady-state points. Subsequently, a synchronization switching condition was developed to model synchronizing switches, aligning them seamlessly with a linearized branch flow problem. Finally, we designed a bottom-up blackstart and restoration framework that considers the switching structure of the DS, energizing/synchronizing switches, DERs with grid-following inverters, and BES-based GFMIs with frequency security constraints. The proposed framework is validated in IEEE-123-bus system, considering cases with two and four GFMIs under various TG recovery instants.

eess.SY↗

arXiv2024

Taylor-Expansion-Based Robust Power Flow in Unbalanced Distribution Systems: A Hybrid Data-Aided Method

Traditional power flow methods often adopt certain assumptions designed for passive balanced distribution systems, thus lacking practicality for unbalanced operation. Moreover, their computation accuracy and efficiency are heavily subject to unknown errors and bad data in measurements or prediction data of distributed energy resources (DERs). To address these issues, this paper proposes a hybrid data-aided robust power flow algorithm in unbalanced distribution systems, which combines Taylor series expansion knowledge with a data-driven regression technique. The proposed method initiates a linearization power flow model to derive an explicitly analytical solution by modified Taylor expansion. To mitigate the approximation loss that surges due to the DER integration and bad data, we further develop a data-aided robust support vector regression approach to estimate the errors efficiently. Comparative analysis in the 13-bus and 123-bus IEEE unbalanced feeders shows that the proposed algorithm achieves superior computational efficiency, with guaranteed accuracy and robustness against outliers.

eess.SY↗

arXiv2024

A Distributionally Robust Optimization Framework for Stochastic Assessment of Power System Flexibility in Economic Dispatch

Given the complexity of power systems, particularly the high-dimensional variability of net loads, accurately depicting the entire operational range of net loads poses a challenge. To address this, recent methodologies have sought to gauge the maximum range of net load uncertainty across all buses. In this paper, we consider the stochastic nature of the net load and introduce a distributionally robust optimization framework that assesses system flexibility stochastically, accommodating a minimal extent of system violations. We verify the proposed method by solving the flexibility of the real-time economic dispatch problem on four IEEE standard test systems. Compared to traditional deterministic flexibility evaluations, our approach consistently yields less conservative flexibility outcomes.

math.OC↗