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Fei Gao

Publications and source records attributed to Fei Gao.

At least 55 records · Page 3Linked to original sources
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

An improved quantum algorithm for ridge regression

Ridge regression (RR) is an important machine learning technique which introduces a regularization hyperparameter $α$ to ordinary multiple linear regression for analyzing data suffering from multicollinearity. In this paper, we present a quantum algorithm for RR, where the technique of parallel Hamiltonian simulation to simulate a number of Hermitian matrices in parallel is proposed and used to develop a quantum version of $K$-fold cross-validation approach, which can efficiently estimate the predictive performance of RR. Our algorithm consists of two phases: (1) using quantum $K$-fold cross-validation to efficiently determine a good $α$ with which RR can achieve good predictive performance, and then (2) generating a quantum state encoding the optimal fitting parameters of RR with such $α$, which can be further utilized to predict new data. Since indefinite dense Hamiltonian simulation has been adopted as a key subroutine, our algorithm can efficiently handle non-sparse data matrices. It is shown that our algorithm can achieve exponential speedup over the classical counterpart for (low-rank) data matrices with low condition numbers. But when the condition numbers of data matrices is large to be amenable to full or approximately full ranks of data matrices, only polynomial speedup can be achieved.

quant-ph↗
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

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

The effect of charm quark on the QCD chiral phase diagram

We study the influence of charm-quark dynamics on the chiral phase structure of Quantum Chromodynamics (QCD) using the recently developed miniDSE scheme for the Dyson-Schwinger equations. By comparing the quark and gluon propagators in the $2+1$- and $2+1+1$-flavor setups within the same truncation scheme, we qualify the impact of the charm-quark loop on the QCD phase diagram. Our results show that the charm quark has only a mild effect on the crossover boundary, which remains almost unchanged within the present setup. The most visible effect is a small shift of the critical endpoint (CEP) toward lower baryon chemical potential, by approximately $3\%$. The present result provides a controlled estimate of the charm-loop effect within the same miniDSE truncation. It indicates that heavy-flavor contributions may become relevant when aiming at precision studies of the CEP location.

hep-ph↗
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

Offline dedicated quantum attacks on block cipher constructions based on two parallel permutation-based pseudorandom functions

Quantum cryptanalysis is essential for evaluating the security of cryptographic systems against the threat of quantum computing. Recently, Shi {\it et al.} introduced a dedicated quantum attack on block cipher constructions based on XOR-type functions, which greatly reduces the required resources (including circuit depth, width, and the number of gates) compared to the parallel Grover-meets-Simon algorithm. Here, our contribution is in two aspects. On the one hand, we discover new cryptographic structures amenable to this attack: PolyMAC and constructions based on two parallel permutation-based pseudorandom functions (TPP-PRFs), including XopEM, SoEM22, SUMPIP, and DS-SoEM, thereby answering Shi {\it et al.}' s open question. On the other hand, for constructions based on TPP-PRFs, we break the obstacle that this attack relies on online query by constructing decoupled XOR-type functions, then propose an offline quantum attack on them. Compared to previous results, our offline attack exhibits significantly reduced query complexity. Specifically, the number of queries to the encryption oracle is reduced from $O(2^{(n+t)/2}\cdot (n-t))$ to $O(2^{t}\cdot (n-t))$ in the quantum query model, where $0<t<n$, $t$ is a truncation parameter, and $n$ is the input length of constructions. Further, we enable its implementation in the classical query model, optimizing both the classical query complexity and time complexity from $\tilde O(2^{2n/3})$ to $\tilde O(2^{(2n-t)/3})$ for $0<t \leq n/2$.

quant-ph↗
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

Dark Matter implications from the LZ, PandaX-4T and XENONnT Data

We investigate a possible dark matter origin of the high-energy nuclear-recoil-like events in data from liquid xenon time projection chamber experiments, including LZ, PandaX-4T, and XENONnT, which cannot be explained by standard elastic spin-independent WIMP scattering. Using our unified DIAMX framework, built on openly available data and likelihood models, we perform the first combined profile-likelihood fits to multiple WIMP-search datasets with a total exposure of approximately 8.8 tonne $\times$ year. We consider two broad classes of dark matter-nucleon interactions, involving either velocity-dependent cross sections or inelastic (endo- and exothermic) scattering, which can reproduce the observed high-energy recoil spectrum, reaching local significances up to $3.5σ$. We further quantify the impact of $^{124}$Xe double electron capture (DEC) backgrounds, finding that variations in the poorly known DEC charge yields can shift the inferred significances from a null-like result to $3.5σ$. We further note that extending the same analysis to data from all three experiments with recoil energies up to $300~\mathrm{keV}$, when available, will provide a powerful test of the dark matter interpretation, since the $^{124}$Xe DEC background is expected to be negligible in this high-energy range.

hep-ph↗
arXiv2026

TriphiBot: A Triphibious Robot Combining FOC-based Propulsion with Eccentric Design

Triphibious robots capable of multi-domain motion and cross-domain transitions are promising to handle complex tasks across diverse environments. However, existing designs primarily focus on dual-mode platforms, and some designs suffer from high mechanical complexity or low propulsion efficiency, which limits their application. In this paper, we propose a novel triphibious robot capable of aerial, terrestrial, and aquatic motion, by a minimalist design combining a quadcopter structure with two passive wheels, without extra actuators. To address inefficiency of ground-support motion (moving on land/seabed) for quadcopter based designs, we introduce an eccentric Center of Gravity (CoG) design that inherently aligns thrust with motion, enhancing efficiency without specialized mechanical transformation designs. Furthermore, to address the drastic differences in motion control caused by different fluids (air and water), we develop a unified propulsion system based on Field-Oriented Control (FOC). This method resolves torque matching issues and enables precise, rapid bidirectional thrust across different mediums. Grounded in the perspective of living condition and ground support, we analyse the robot's dynamics and propose a Hybrid Nonlinear Model Predictive Control (HNMPC)-PID control system to ensure stable multi-domain motion and seamless transitions. Experimental results validate the robot's multi-domain motion and cross-mode transition capability, along with the efficiency and adaptability of the proposed propulsion system.

cs.RO↗
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

High-Speed Vision-Based Flight in Clutter with Safety-Shielded Reinforcement Learning

Quadrotor unmanned aerial vehicles (UAVs) are increasingly deployed in complex missions that demand reliable autonomous navigation and robust obstacle avoidance. However, traditional modular pipelines often incur cumulative latency, whereas purely reinforcement learning (RL) approaches typically provide limited formal safety guarantees. To bridge this gap, we propose an end-to-end RL framework augmented with model-based safety mechanisms. We incorporate physical priors in both training and deployment. During training, we design a physics-informed reward structure that provides global navigational guidance. During deployment, we integrate a real-time safety filter that projects the policy outputs onto a provably safe set to enforce strict collision-avoidance constraints. This hybrid architecture reconciles high-speed flight with robust safety assurances. Benchmark evaluations demonstrate that our method outperforms both traditional planners and recent end-to-end obstacle avoidance approaches based on differentiable physics. Extensive experiments demonstrate strong generalization, enabling reliable high-speed navigation in dense clutter and challenging outdoor forest environments at velocities up to 7.5 m/s}.

cs.RO↗
arXiv2026

Screening masses of positive- and negative-parity hadron ground-states, including those with strangeness

Using a symmetry-preserving treatment of a vector $\times$ vector contact interaction (SCI) at nonzero temperature, we compute the screening masses of flavour-SU(3) ground-state $J^P=0^\pm$, $1^\pm$ mesons, and $J^P=1/2^\pm$, $3/2^\pm$ baryons. We find that all correlation channels allowed at $T=0$ persist when the temperature increases, even above the QCD phase transition. The results for mesons qualitatively agree with those obtained from the contemporary lattice-regularised quantum chromodynamics (lQCD) simulations. One of the most remarkable features is that each parity-partner-pair degenerates when $T>T_c$, with $T_c$ being the critical temperature. For each pair, the screening mass of the negative parity meson increases monotonously with temperature. In contrast, the screening mass of the meson with positive parity is almost invariant on the domain $T\lesssim T_c/2$; when $T$ gets close to $T_c$, it decreases but soon increases again and finally degenerates with its parity partner, which signals the restoration of chiral symmetry. We also find that the $T$-dependent behaviours of baryon screening masses are quite similar to those of the mesons. For baryons, the dynamical, nonpointlike diquark correlations play a crucial role in the screening mass evolution. We further calculate the evolution of the fraction of each kind of diquark within baryons respective to temperature. We observe that, at high temperatures, only $J=0$ scalar and pseudoscalar diquark correlations can survive within $J^P=1/2^\pm$ baryons.

hep-ph↗
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↗