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Fei Gao

Publications and source records attributed to Fei Gao.

At least 19 recordsLinked to original sources
arXiv2026

Controlling Hydrogen Isotope Retention at Helium Cavities through Radiation-Induced Segregation in Fusion Steels

Hydrogen isotope retention in plasma facing and structural alloys is a central materials challenge for deuterium-tritium fusion. Motivated by ion beam irradiation experiments and first principles calculations, we identify an irradiation-enabled mechanism whereby solute segregation to defect sinks enhances hydrogen isotope trapping. Triple-ion irradiation of reduced activation ferritic-martensitic steel F82H reveals pronounced segregation of Cr and Ta to cavity surfaces. Density functional theory shows that these segregants markedly increase H stability at cavities by strengthening binding energies and increasing migration barriers, leading to substantially higher attention. The effect originates from solute-tuned electronic structure: Ta promotes strong H 1s-metal d orbital hybridization, whereas Cr shifts the surface d-band toward a more favorable bonding configuration. These findings provide an atomistic link between irradiation-induced segregation and elevated-temperature hydrogen isotope retention and alloy chemistry routes to control tritium inventory in fusion environments.

cond-mat.mtrl-sci↗
arXiv2026

Quantum hot carrier spectra in plasmonic catalysis

Vibrational activation of admolecules on metal nanoparticles is an elementary step in plasmonic catalysis, yet the underlying dynamics driven by hot carriers is not fully understood in the quantum regime. Using an atomistic description of plasmonic hot carrier generation, we investigate vibrational excitation and dissociation of oxygen on silver nanoparticles as a function of diameter D. As D reduces from the classical to quantum-sized regime, quantized distribution of hot carriers emerges with increasing population in the high-energy regions. These highly energetic hot carriers deliver more efficient vibrational coupling and dissociation. The rate of vibrational excitation shows a linear 1/D scaling, which results from Landau damping. It turns nonlinear at elevated light intensities due to vibrational heating generated by multiple electron scattering. The finding of quantized distribution of hot carrier in plasmonic catalysis opens new avenues for selective control and nonthermal energy conversion.

cond-mat.mtrl-sci↗
arXiv2026

GIF: Agentic Generation of Interactive and Functional Object Compositions for Robot Learning

Robot manipulation foundation models require scalable evaluation and data generation across diverse scenarios, with simulation providing an environment for both. Automated scene generation offers a promising path, yet prior work has largely emphasized coarse-grained scene layouts rather than fine-grained functional object compositions. Motivated by this gap, we present GIF, an agentic Generation framework for Interactive and Functional object compositions. In this framework, we recast this problem as disentangled reconstruction followed by relative pose recovery. CoGen produces instance-disentangled meshes with coarse initial poses leveraging complementary strengths of 2D and 3D generative models. GPRM refines the relative pose under joint geometric and physical guidance, and a VLM verifier selects the candidate that best matches the structured specification. We further construct a benchmark spanning eight representative contact-geometry classes and compare with state-of-the-art generators; GIF improves both asset quality and relation matching, while reducing collision rate to below 1%. Finally, we synthesize data for policy learning, revealing diversity scaling in both simulation and real-world deployment.

cs.RO↗
arXiv2026

HyperSketch: Controllable Video Sketching in a Style Hyperspace

Vector sketch animation offers tremendous advantages for multimedia and creative design through concise line expressions and flexible editing. Learning-based generation methods of sketch animation have made significant progress in the last decade, but still suffer from limited style diversity and controllability. This paper presents a controllable video sketching method that automatically converts videos into multi-style vector sketch animations. A continuous style hyperspace is constructed by multi-dimensional sketch styles (fidelity, simplicity, text guidance strength) and the timeline. With this hyperspace, stroke control points are parameterized as 4-variable Bernstein polynomials, ensuring smooth and differentiable style transitions. A multi-task, multi-stage optimization framework is designed to learn stroke hyperparameters accurately and efficiently. We further developed a web-based interactive interface that allows real-time style manipulation via editable curves. Experiments show the style controllability, high-quality, and user-friendliness of our method, which outperforms SOTA methods.

cs.GR↗
arXiv2026

Neural-Primitive: An Efficient End-to-end Local Planner with Primitive-based Imitation Learning for Autonomous Flight

Autonomous flight in unknown cluttered environments is hindered by the computation-quality-memory trilemma of onboard trajectory generation. In this paper, we propose an efficient end-to-end local planner via imitation learning. A lightweight offline-primitive-based dataset collection framework is designed to produce safe and high-quality trajectory primitives in non-convex environments. A compact neural network directly maps sensory inputs to polynomial coefficients that inherently encode higher-order dynamical information. The learned policy generates smooth, empirically collision-free and dynamically feasible trajectories in real time without back-end solving. It achieves ultra-fast computation (below 1ms on a standard desktop and average 3.68ms during onboard flight), while maintaining low onboard memory requirements (less than 1.5MiB). Extensive simulation benchmarks demonstrate superiority in both planning latency and target-reaching progress quality. Zero-shot deployment in real-world experiments further validates the robust sim-to-real transfer capability of the proposed method.

cs.RO↗
arXiv2026

DPNet: Efficient Dead-End Prediction and Avoidance for Vision-Based UAV Navigation

Vision-based Unmanned Aerial Vehicles (UAVs) often suffer from navigation failures in dead ends due to limited sensing accuracy and range. To address this challenge, this paper proposes a systematic solution for efficient dead-end prediction and avoidance. The proposed method introduces a lightweight neural network to predict the relative distance and bearing of potential dead ends within the current field of view using RGB-D inputs. These predictions prune a predefined, compact trajectory library, enabling the planner to proactively avoid dead ends while maintaining navigational smoothness. Notably, our approach transfers across real-world scenarios without manual annotation or fine-tuning on real-world data. The system achieves high-frequency replanning at 50 Hz onboard. Extensive simulation benchmarks demonstrate superior performance in success rate, flight time, and trajectory length, and real-world experiments further validate its effectiveness in complex scenarios.

cs.RO↗
arXiv2026

Self-Synchronized Terahertz and X-Ray Free-Electron Lasers from a Single Pre-Bunched Electron Beam

Ultrafast pump-probe spectroscopy combining intense terahertz (THz) and X-ray pulses is a critical tool for investigating complex structural and electronic dynamics in materials. However, current setups combining THz sources and X-ray free-electron lasers (FELs) often suffer from high system complexity, inherent timing jitter, or limited THz pulse properties. Here, we experimentally demonstrate the generation of intrinsically synchronized, strong-field, narrow-band THz and X-ray FELs from a single pre-bunched electron beam. Sequentially passing the beam through X-ray and THz amplifiers reveals a highly synergistic process: the initial periodic THz density modulation notably boosts the X-ray FEL pulse energy, while robustly surviving the intense X-ray emission to drive high-power, narrow-band THz radiation. Originating from the same electron bunch, the two pulses inherently maintain a precise, constant time delay. This jitter-free scheme establishes a highly reliable platform tailored for both X-ray-pump/THz-probe and THz-pump/X-ray-probe experiments.

physics.acc-ph↗
arXiv2026

AgilePE: Autonomous UAV Pursuit-Evasion via Self-Play Reinforcement Learning

Autonomous pursuit-evasion is a fundamental challenge for Unmanned Aerial Vehicles (UAVs), requiring rapid decision-making under tightly coupled dynamics and continuously changing opponent behaviors. Traditional rule-based or differential-game approaches often struggle with high-dimensional aerial interactions and agile maneuvering. We present AgilePE, a complete system for autonomous UAV pursuit-evasion via self-play reinforcement learning. AgilePE integrates agile low-level control, competitive policy optimization, and sim-to-real deployment in a unified framework. The policy directly maps onboard state observations to Collective Thrust and Body Rates (CTBR) commands, enabling end-to-end agile maneuvering without intermediate trajectory planners or waypoint controllers. For training, we use competitive self-play with Prioritized Fictitious Self-Play (PFSP) and a diversified opponent pool, enabling agents to improve against historical policies while stabilizing optimization and reducing policy oscillation. This process leads to the emergence of sophisticated pursuit and evasion strategies. For real-world deployment, we develop a hardware-aligned simulation pipeline that models actuator-response dynamics, communication latency, and domain randomization. The learned policies transfer zero-shot to real quadrotors without task-specific tuning. Real-world experiments reproduce pursuit-evasion tactics observed in simulation, including rapid dodging and flanking, and demonstrate interactive two-agent zero-shot deployment.

cs.RO↗
arXiv2026

D-VLC: Decentralized Vision-Language Collaboration for Heterogeneous Embodied Multi-Robot Systems in Unknown Environments

Multi-robot systems, particularly heterogeneous robot swarms, can improve the efficiency of complex task execution through parallel collaboration and complementary capabilities. However, conventional rule-based methods rely on predefined task models and specialized decision making programs, making it difficult to understand complex semantic instructions and coordinate heterogeneous robots. LLMs introduce strong language understanding and task reasoning capabilities, allowing multi-robot systems to interpret instructions, decompose tasks, and assign roles according to task semantics. VLMs further incorporate visual perception, enabling robots to reason about objects, regions, and spatial relationships in physical environments. Nevertheless, existing LLM/VLM based methods often depend on known maps, centralized and synchronized decision making, limiting their generalization to heterogeneous robots and unseen tasks. We therefore propose a framework that combines decentralized asynchronous reasoning, lightweight information sharing, capability aware collaboration, and a unified action interface, enabling general purpose VLMs to generate robot specific actions executed by learning free experts without task or robot specific training. Experiments across diverse scenarios and multiple VLMs show success rates above 70\%, with completion time reduced by up to 55.8\% relative to the geometric greedy baseline.

cs.RO↗
arXiv2026

Robust Activation Map Rectification for Weakly Supervised Volumetric Segmentation: Temporal Coherence as a Free Lunch

Weakly supervised segmentation relies heavily on class activation maps (CAMs) to initially localize target regions. However, CAMs are often noisy and prone to catastrophic failures. Existing remedies typically introduce additional training stages or prototype learning, increasing computational cost and reducing robustness. In this paper, we propose a training-free prototype-free framework that rectifies unreliable CAMs by exploiting temporal and structural coherence in volumetric data as a free lunch. Our approach is built on two key components. First, we introduce Variance-Reduced Activation Aggregation (VRAA) which suppresses noise and amplify coherent semantic signals. We provide a theoretical justification by modeling CAMs as high-dimensional random vectors and show that aggregation yields provable variance reduction. Second, we design a Bidirectional Extremity Rectification (BER) mechanism that detects and rectifies implausible activations through bidirectional extremity checks, effectively mitigating extreme-value failures without learning additional parameters. Our method is model-agnostic and can be seamlessly integrated with existing pipelines. Extensive experiments on multiple public benchmarks demonstrate substantial improvements over state-of-the-art weakly supervised methods, achieving up to 20% Dice and 40% mIoU gains while reducing inference time by more than 5 times. These results indicate that leveraging coherence as an implicit inductive bias yields a principled and efficient approach to stabilizing weakly supervised volumetric segmentation. Our code will be available.

cs.CV↗
arXiv2026

Test Case Prioritization for DNNs via Neural Collapse Instability

With the widespread deployment of deep neural networks (DNNs) in safety-critical domains, reducing the cost of model validation under limited testing budgets has become increasingly important. Existing test case prioritization techniques often rely on single-checkpoint confidence signals derived from output probabilities. However, DNNs can be confidently wrong, and the confidence margin between the predicted and competing classes is frequently small, which weakens early fault discovery. To address this limitation, we propose a Neural-Collapse-Inspired Prioritization (NCIP) framework that replaces absolute confidence with cross-checkpoint prediction variability in the terminal training regime, where model geometry becomes highly structured. NCIP introduces two key components. First, it selects an NC-guided representative subset of training checkpoints using an equiangularity score of classifier weights, quantified as the standard deviation of pairwise cosine similarities among class weight vectors. Second, it prioritizes test inputs by their prediction variability across the selected checkpoints, surfacing boundary-adjacent and failure-prone samples that are unstable under checkpoint-induced decision boundary shifts. Extensive experiments across multiple datasets and architectures show that NCIP achieves strong performance in early fault discovery compared with competitive baselines, with 1.5 to 16.6 percent RAUC-ALL gains and 4.9 to 20.6 percent RAUC-500 gains under the same testing budget. NCIP further attains the best average performance across all dataset-model pairs.

cs.LG↗
arXiv2026

FLOAT Drone for Physical Interaction: Lateral Airflow Reduction, Wrench Modeling, and Adaptive Control

Aerial physical interaction represents a promising direction for next-generation unmanned aerial vehicles (UAVs), but it requires an aerial platform that can exert contact forces while maintaining stable flight. For close-proximity tasks, this translates into three coupled design requirements: multidimensional wrench generation for stable contact, compactness for maneuverability and safety in confined spaces, and reduced lateral airflow toward the target when generating horizontal force. This article presents FLOAT Drone, a fully actuated coaxial UAV with servo-driven control surfaces for close-proximity physical interaction. The coaxial dual-rotor layout provides a compact propulsion layout, while the control surfaces, immersed in the rotor downwash, generate lateral forces and moments for 6-DoF wrench generation. A force-matched computational fluid dynamics (CFD) comparison with a tilted-rotor alternative quantifies the reduction in target-facing lateral airflow. To account for nonlinear rotor--control-surface coupling in the rotor wake, a high-fidelity polynomial aerodynamic wrench model is identified from precision force measurements and embedded in a constrained nonlinear allocator for real-time wrench tracking. Comparative flight and interaction experiments show that the proposed framework improves control accuracy over linear allocation baselines, rejects ground-effect and payload disturbances, and enables close-proximity drawer push--pull manipulation through a $2~\mathrm{cm}$ handle clearance.

cs.RO↗
arXiv2026

Sphere-VIO: Fast and Robust Visual-Inertial Odometry via Unified Spherical Representation for Heterogeneous Multi-Camera Systems

Multi-camera visual-inertial odometry (VIO) overcomes the inherent limitations of pure visual systems by expanding the field of view. However, existing algorithms are typically tailored for fixed camera setups and lack unified compatibility with heterogeneous multi-camera systems. Meanwhile, due to the absence of a unified cross-camera representation and association mechanism, current methods struggle to achieve a balance among robust cross-camera feature tracking, stable depth estimation, and reliable real-time performance. To address these issues, we present Sphere-VIO, a lightweight filter-based VIO framework with unified spherical representation for heterogeneous multi-camera systems. Specifically, we first propose a Unified Spherical Panorama Model (USPM) that supports all standard camera models and enables bidirectional fast mapping between multi-camera images and a shared spherical space without sequential stitching, simplifying cross-camera feature management and improving triangulation efficiency. Second, we design a parallel-accelerated depth-guided semi-direct tracking pipeline, namely Hierarchical Omnidirectional Feature Alignment (HOFA), with global spherical constraints for robust cross-camera matching, and fuse multi-camera depth observations into a standard depth filter for stable initialization. Finally, we develop a multi-camera-adapted ESKF backend that employs spherical bearing residuals and Schur complement marginalization to minimize computational overhead, enabling accurate real-time state estimation on resource-constrained devices. Extensive experiments on public benchmarks and a custom omnidirectional dataset show that Sphere-VIO achieves superior trade-offs between accuracy, robustness, efficiency, and cross-camera generality.

cs.RO↗
arXiv2026

Topological phase transition driven by in-plane spin rotation

The intrinsic coupling between magnetism and nontrivial band topology in magnetic topological insulators makes external magnetic fields a powerful tool for manipulating topological states. However, conventional magnetic control mechanisms, such as driving magnetic phase transitions or fully reversing magnetization, typically demand large magnetic fields and lack continuous tunability. Here, we establish a symmetry framework for the reversible switching of topological states via continuous in-plane spin rotation, governed by magnetic point group constraints on the Berry curvature distribution. Using a two-dimensional kagome ferromagnetic Chern insulator as a prototype, we demonstrate that a 60{\deg}in-plane magnetization rotation reverses the sign of the Chern number, transitioning through a topologically trivial state. Crucially, micromagnetic simulations confirm that this spin-reorientation-driven switching operates under exceptionally small magnetic fields and on ultrafast timescales. This work provides a highly efficient, low-energy paradigm for the manipulation of topological states.

cond-mat.mtrl-sci↗
arXiv2026

Axial-Vector Lattice Benchmarks Reveal a Common Medium Response of Meson Screening in Hot QCD

Meson screening masses trace the evolution of hadronic correlations toward quasi-free quark--antiquark screening in hot QCD. Combining lattice QCD (LQCD) benchmarks with a symmetry-preserving Dyson--Schwinger baseline, we identify a flavor-dependent axial-vector quasi-free onset, $x^\ast_{fg}=T^\ast_{fg}/T_c$: an operational high-temperature matching scale at which the axial-vector screening mass has approached the corresponding free-field value after ordinary chiral restoration or parity-partner convergence has set in. On the finite interval $1\le x\lesssim x^\ast_{fg}$, independent light/strange and charm-containing lattice benchmarks are organized by a common medium-response function with one flavor-sector parameter. One axial-vector point fixes this parameter; the remaining axial-vector data test its temperature dependence, and vector screening masses validate it without vector input. A reduced-mass interpolation then yields lattice-testable quasi-free onsets and screening spectra for light-charm and bottom-containing sectors. The resulting onset scales provide common reference points for future lattice and continuum studies of meson dissolution across flavor.

hep-ph↗
arXiv2026

From Magnetic to Inverse Magnetic Catalysis: The Interplay of Quark and Gluon Mass Generation in Magnetic Fields

We analyze the effects of the magnetic field on the quark and gluon propagators within the functional QCD framework. By solving the coupled Dyson-Schwinger equations for the quark and gluon propagators, we find that the quark mass is generally enhanced in the presence of a magnetic field, leading to magnetic catalysis of the chiral condensate. Meanwhile, the magnetic field also induces an increase in the gluon screening mass. The enhancement of the gluon screening mass suppresses the quark-gluon interaction and thereby weakens the strength of dynamical chiral symmetry breaking, establishing a competing mechanism against magnetic catalysis. In particular, this enhancement of the gluon screening mass becomes dominant near the chiral phase transition, which in turn gives rise to inverse magnetic catalysis.

hep-ph↗
arXiv2026

FLAP: FOV-Constrained Active Perception Planning for Prior-Map-Free 3D Navigation

Safe and efficient trajectory planning in unknown, cluttered 3D environments constitutes a critical bottleneck for deploying Unmanned Aerial Vehicles (UAVs) in real-world applications. This challenge is further exacerbated by the limited field-of-view (FOV) and sensing range of onboard sensors. Many existing methods either make simplistic assumptions about unexplored space or rely on conservative heuristics such as speed limits or fixed perception patterns, reducing efficiency and generalizing poorly across different sensor types. In this work, we propose a novel planning framework that directly integrates active perception into trajectory optimization, thereby improving safety while preserving efficiency. The perception constraints are derived from the UAV's dynamic model and formulated in the sensor coordinate frame, which enables precise handling of FOV geometry. The velocity-triggered activation mechanism enables the planner to balance perception and motion efficiency. We introduce an active perception sub-trajectory segment with parametric start-time optimization, mitigating collision risks from late obstacle detection. Our formulation enables active perception during arbitrary 3D maneuvers, extending beyond prior methods designed mainly for horizontal motion. All constraints and penalties are incorporated into a differentiable optimization problem, so the planner requires only a simple front-end global path for guidance, rather than a computationally expensive perception-aware path generator. Extensive simulations and real-world experiments demonstrate robust performance across diverse unknown environments with varying sensor configurations.

cs.RO↗
arXiv2026

Agile Fall Recovery for Quadrotors with Bidirectional Thrust via Reinforcement Learning

Autonomous fall recovery is a critical capability for quadrotors operating in real-world environments, where collisions or failures may leave the vehicle resting on the ground in an arbitrary attitude. This problem is challenging because recovery must be achieved under limited onboard sensing, in constrained free space, with ground contact, and in the presence of unknown disturbances. In this letter, we present an RL-based framework for autonomous fall recovery of a quadrotor from arbitrary ground attitudes to stable hover using only lightweight onboard sensors. To address severe partial observability and intermittent sensor invalidity, we train a recurrent policy within an asymmetric actor--critic architecture, leveraging an Incremental Nonlinear Dynamic Inversion (INDI) controller to track the policy output. Combined with high-fidelity simulations of motor response and optical flow, the overall training framework significantly reduces the sim-to-real gap. Simulation ablation studies validate the importance of the main design choices, while real-world experiments demonstrate zero-shot transfer and robust recovery under different initial attitudes, wind disturbances, and additional payloads. These results demonstrate that agile quadrotor fall recovery can be achieved without explicit state estimation using only limited and unreliable onboard sensing.

cs.RO↗