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Ye Zhou

Publications and source records attributed to Ye Zhou.

At least 19 recordsLinked to original sources

Distance Energies and Negative Type of Flat Tori

Let \(T_\Lambda=\mathbb R^d/\Lambda\), \(d\geq2\), be a flat torus with quotient metric \(\rho_\Lambda\), and consider the distance energies \(I_\alpha(\mu)=\iint \rho_\Lambda(x,y)^\alpha\,d\mu(x)\,d\mu(y)\) of Borel probability measures \(\mu\). We prove a quantitative Fourier signature of the cut locus: for every Voronoi facet and every \(\alpha>0\), there is a sequence of dual-lattice frequencies approaching the facet normal along which the Fourier coefficients of \(\rho_\Lambda^\alpha\) are positive, with an explicit leading asymptotic determined by the facet. As direct consequences, Haar measure is not a local maximizer for any positive distance power, even among smooth densities, and every flat torus of dimension at least two has supremal negative type and generalized roundness zero. We then solve the global maximization problem for two classes of flat tori. On an orthogonal rectangular torus, the maximizers undergo a transition at \(\alpha=2\): for \(1\leq\alpha<2\) they are the translated uniform measures on the two-torsion subgroup; at \(\alpha=2\) all balanced couplings on translates of that subgroup are extremal; and for \(\alpha>2\) only equally weighted diametral pairs remain. On the regular hexagonal torus, the maximizers are precisely the uniform measures on translates of a distinguished cyclic subgroup of order three for every \(\alpha\geq1\).

math-ph

Cutoff-Stable Null Convergence and Directional Rigidity in Bianchi I Spacetimes

We derive an endpoint-free optical rigidity theorem and use it to separate two rigidity regimes in Bianchi I spacetimes, without assuming that the spatial metric is diagonal in a fixed basis. For a smooth, regular, twist-free null congruence, the affine Raychaudhuri equation expresses a finite null-Ricci integral as an expansion boundary term minus a nonnegative optical bulk. If the past and future cutoffs are removed independently and their two-end liminf is nonnegative, two-sided completeness forces the optical tensor and the null Ricci contraction to vanish pointwise. In Bianchi I this freezes the spatial metric on the fixed kernel of the conserved covector. We classify the resulting saturation geometry: a non-static metric has zero, one, or two unoriented saturated lines, equivalently zero, two, or four oriented rays, and a third line forces staticity. Under the stronger pointwise null convergence condition, the existence of a single two-sided complete null geodesic already forces the spatial metric to be constant and the cosmic-time interval to be all of $\mathbb R$; on the Cartesian universal cover the spacetime is Minkowski. Periodic models attain the four-ray saturation bound in the weaker cutoff-stable regime, with $I_p^{\mathrm{ind}}=-\infty$ in every nonsaturated direction, while pointwise null convergence fails on open time intervals. Matter and achronal averaged-null-energy consequences are stated under an explicit matching assumption on field equations and cutoff prescriptions.

gr-qc

Embedding dependence of fermion mass hierarchies in $S_3$-symmetric Yukawa sectors

When irreducible representations occur with multiplicity, a family symmetry fixes the representation content of a Yukawa sector but need not fix its embedding in the space of generation tensors. We study the spectral consequences of this additional embedding data for complex-symmetric three-family tensors with the $S_3$ assignment $V=\mathbf{1}\oplus\mathbf{2}$. Since $\operatorname{Sym}^2V=2\,\mathbf{1}\oplus2\,\mathbf{2}$, three-dimensional invariant Yukawa subspaces of type $\mathbf{1}\oplus\mathbf{2}$ form a continuous $\mathbb{CP}^1\times\mathbb{CP}^1$ family, denoted $\mathcal L_{t,\kappa}$ on a finite affine chart. On the $\kappa=0$ branch, we solve the inverse singular-value problem and obtain necessary and sufficient conditions for any prescribed ordered positive mass spectrum. A fixed singlet embedding imposes a finite hierarchy bound, whereas varying the embedding accommodates every positive three-family spectrum. For general embeddings, $\mathcal L_{t,\kappa}$ contains a nonzero rank-one matrix if and only if $2t\kappa^2=1$; equivalently, these are precisely the finite-chart embeddings for which $\sigma_3/\sigma_2$ is unbounded. In a conventional complex-symmetric $S_3$ three-Higgs sector, the same condition becomes $y_2y_5^2=\sqrt{2}\,y_1y_3^2$, so order-one Yukawa coupling ratios can support arbitrarily large mass hierarchies. We then apply the result to a recent Clifford-algebraic construction and show that its family-resolved Yukawa tensors span $\mathcal L_{2,0}$, whose singular values obey $m_1+m_2\leq m_3\leq m_1+3m_2$. This excludes the observed charged-lepton hierarchy for arbitrary complex vacuum alignment within the existing Higgs directions. The resulting obstruction is therefore tied to the fixed multiplicity embedding rather than to the $S_3$ representation content alone.

hep-ph

Open-Channel Operator Closure of the Finite-Cutoff JT Gravity Disk Amplitude

The finite-cutoff disk amplitude of Jackiw-Teitelboim (JT) gravity is known from closed-channel spectral methods and finite-cutoff trumpet/cap gluing, while its complete open-channel operator formulation has remained incomplete. In this paper, we provide an operator-level open-channel closure of this known result. More precisely, we separate the data imported from finite-cutoff geometry -- the rigid length --momentum kernel, the disk-trumpet gluing relation, and hence the target cap overlap -- from the structures derived within the parity-even auxiliary problem, namely the Neumann vacuum sector, the generalized eigenbasis, and the branch-projecting spectral functional. When these ingredients are combined, the known finite-cutoff disk amplitude is reproduced as a boundary-state matrix element. We further show that the induced finite-cutoff geodesic sector is bandlimited and therefore admits sampled and branch-doubled discrete representations of the same physical sector, rather than an independent microscopic lattice model. Finally, we show that the resulting compact-support branch-difference amplitude is not the ordinary thermal trace of any single lower-bounded self-adjoint $\beta$-independent Hamiltonian.

gr-qc

Exact quasinormal residues and double poles from hypergeometric connection formulas

We develop a unified mathematical method for the pole structure of frequency-domain Green's functions and the associated quasinormal spectra in radial boundary value problems reducible to the Gauss hypergeometric equation. By systematically employing connection formulas for Kummer solutions, we construct an explicit quantization function that encodes arbitrary linear asymptotic boundary conditions. We demonstrate that the frequency-dependent spectral factor entering the residue formula is controlled algebraically by the closed-form Digamma derivative of this quantization function, bypassing integral evaluation. Furthermore, we establish the simultaneous vanishing of the quantization function and its first derivative as a direct algebraic criterion for double-pole QNMs. The formalism is successfully benchmarked against the exact BTZ black hole spectrum and provides an analytic diagnostic for the exceptional lines and nearly double-pole excitations in the Nariai/P\"oschl-Teller limit.

gr-qc

Mitigation of Incoherent Spectral Lines via Adaptive Coherence Analysis for Continuous Gravitational-Wave Searches

The sensitivity of continuous gravitational-wave searches is strictly limited by non-Gaussian spectral artefacts that accumulate coherent power over long observation baselines. In this paper, we present an unsupervised mitigation framework based on adaptive network coherence analysis. Unlike traditional veto methods that discard entire frequency bands, our pipeline selectively suppresses local artefacts while preserving global potentially astrophysical signals. We validate the method using Advanced LIGO O3 data, analysing the cleaning performance across integration times of 1, 3, and 5 days. For the 5-day dataset, the pipeline identifies and mitigates 89\% and 77\% of the total spectral lines in the Hanford and Livingston detectors, respectively, while effectively preserving the coherent population consistent with astrophysical morphologies. This is achieved while modifying less than 7\% of the analysis bandwidth spanning 20~Hz to 2000~Hz. Rigorous statistical verification demonstrates that the mitigation effectively suppresses the non-Gaussian tail of the noise distribution while strictly preserving the statistical integrity of coherent signal candidates. By recovering detector sensitivity in parameter spaces previously contaminated by the spectral forest, this framework provides a robust preprocessing strategy for all-sky searches.

gr-qc

Vertical Planetary Landing on Sloped Terrain Using Optical Flow Divergence Estimates

Autonomous landing on sloped terrain poses significant challenges for small, lightweight spacecraft, such as rotorcraft and landers. These vehicles have limited processing capability and payload capacity, which makes advanced deep learning methods and heavy sensors impractical. Flying insects, such as bees, achieve remarkable landings with minimal neural and sensory resources, relying heavily on optical flow. By regulating flow divergence, a measure of vertical velocity divided by height, they perform smooth landings in which velocity and height decay exponentially together. However, adapting this bio-inspired strategy for spacecraft landings on sloped terrain presents two key challenges: global flow-divergence estimates obscure terrain inclination, and the nonlinear nature of divergence-based control can lead to instability when using conventional controllers. This paper proposes a nonlinear control strategy that leverages two distinct local flow divergence estimates to regulate both thrust and attitude during vertical landings. The control law is formulated based on Incremental Nonlinear Dynamic Inversion to handle the nonlinear flow divergence. The thrust control ensures a smooth vertical descent by keeping a constant average of the local flow divergence estimates, while the attitude control aligns the vehicle with the inclined surface at touchdown by exploiting their difference. The approach is evaluated in numerical simulations using a simplified 2D spacecraft model across varying slopes and divergence setpoints. Results show that regulating the average divergence yields stable landings with exponential decay of velocity and height, and using the divergence difference enables effective alignment with inclined terrain. Overall, the method offers a robust, low-resource landing strategy that enhances the feasibility of autonomous planetary missions with small spacecraft.

cs.RO

DIAP: A Decentralized Agent Identity Protocol with Zero-Knowledge Proofs and a Hybrid P2P Stack

The absence of a fully decentralized, verifiable, and privacy-preserving communication protocol for autonomous agents remains a core challenge in decentralized computing. Existing systems often rely on centralized intermediaries, which reintroduce trust bottlenecks, or lack decentralized identity-resolution mechanisms, limiting persistence and cross-network interoperability. We propose the Decentralized Interstellar Agent Protocol (DIAP), a novel framework for agent identity and communication that enables persistent, verifiable, and trustless interoperability in fully decentralized environments. DIAP binds an agent's identity to an immutable IPFS or IPNS content identifier and uses zero-knowledge proofs (ZKP) to dynamically and statelessly prove ownership, removing the need for record updates. We present a Rust SDK that integrates Noir (for zero-knowledge proofs), DID-Key, IPFS, and a hybrid peer-to-peer stack combining Libp2p GossipSub for discovery and Iroh for high-performance, QUIC based data exchange. DIAP introduces a zero-dependency ZKP deployment model through a universal proof manager and compile-time build script that embeds a precompiled Noir circuit, eliminating the need for external ZKP toolchains. This enables instant, verifiable, and privacy-preserving identity proofs. This work establishes a practical, high-performance foundation for next-generation autonomous agent ecosystems and agent-to-agent (A to A) economies.

cs.DC

MoCom: Motion-based Inter-MAV Visual Communication Using Event Vision and Spiking Neural Networks

Reliable communication in Micro Air Vehicle (MAV) swarms is challenging in environments, where conventional radio-based methods suffer from spectrum congestion, jamming, and high power consumption. Inspired by the waggle dance of honeybees, which efficiently communicate the location of food sources without sound or contact, we propose a novel visual communication framework for MAV swarms using motion-based signaling. In this framework, MAVs convey information, such as heading and distance, through deliberate flight patterns, which are passively captured by event cameras and interpreted using a predefined visual codebook of four motion primitives: vertical (up/down), horizontal (left/right), left-to-up-to-right, and left-to-down-to-right, representing control symbols (``start'', ``end'', ``1'', ``0''). To decode these signals, we design an event frame-based segmentation model and a lightweight Spiking Neural Network (SNN) for action recognition. An integrated decoding algorithm then combines segmentation and classification to robustly interpret MAV motion sequences. Experimental results validate the framework's effectiveness, which demonstrates accurate decoding and low power consumption, and highlights its potential as an energy-efficient alternative for MAV communication in constrained environments.

cs.CV

Hierarchical Deep Deterministic Policy Gradient for Autonomous Maze Navigation of Mobile Robots

Maze navigation is a fundamental challenge in robotics, requiring agents to traverse complex environments efficiently. While the Deep Deterministic Policy Gradient (DDPG) algorithm excels in control tasks, its performance in maze navigation suffers from sparse rewards, inefficient exploration, and long-horizon planning difficulties, often leading to low success rates and average rewards, sometimes even failing to achieve effective navigation. To address these limitations, this paper proposes an efficient Hierarchical DDPG (HDDPG) algorithm, which includes high-level and low-level policies. The high-level policy employs an advanced DDPG framework to generate intermediate subgoals from a long-term perspective and on a higher temporal scale. The low-level policy, also powered by the improved DDPG algorithm, generates primitive actions by observing current states and following the subgoal assigned by the high-level policy. The proposed method enhances stability with off-policy correction, refining subgoal assignments by relabeling historical experiences. Additionally, adaptive parameter space noise is utilized to improve exploration, and a reshaped intrinsic-extrinsic reward function is employed to boost learning efficiency. Further optimizations, including gradient clipping and Xavier initialization, are employed to improve robustness. The proposed algorithm is rigorously evaluated through numerical simulation experiments executed using the Robot Operating System (ROS) and Gazebo. Regarding the three distinct final targets in autonomous maze navigation tasks, HDDPG significantly overcomes the limitations of standard DDPG and its variants, improving the success rate by at least 56.59% and boosting the average reward by a minimum of 519.03 compared to baseline algorithms.

cs.RO

High efficiency, high quality factor active membrane metasurfaces with extended Kerker effect

Efficient, low-power, and highly integrated optoelectronic devices remain a critical yet challenging goal.Here, we introduce the extended Kerker effect paradigm that synergizes Kerker's condition with quasi-bound states in the continuum (q-BICs) to overcome these limitations. By engineering dual-mode dispersion, we achieve a high efficiency beam deflector using a membrane metasurface, simultaneously realizing robust parameter tolerance and narrow-linewidth resonances-two typically conflicting properties.Our experiment demonstrates an absolute beam deflection efficiency exceeding 92%, with exceptional spectral and spatial selectivity, including a 4 GHz linewidth, a 2.8o divergence angle, and a quality factor of 114. Additionally, it enables 94% transmission intensity modulation at a pump intensity as low as 0.5 W/cm2 in experiments. The extended Kerker effect provides a scalable platform for energy-efficient and integrable optoelectronic devices, paving the way for transformative advancements in next-generation wireless communications and LiDAR.

physics.optics

Dual Synchronization Effects in Light Scattering by Spherical Particle Systems

We report the discovery of a novel and fundamental dual synchronization relationship between the scattering efficiency (Q$_{\text{sca}}$) and a specifically formulated angular distribution complexity parameter ($\widetilde{C}_{\text{p}}$) in spherical particle systems. Through extensive numerical simulations using the rigorous Multiple Sphere T-Matrix (MSTM) method, we found that Q$_{\text{sca}}$ exhibits a strong positive correlation with (1-$\widetilde{C}_{\text{p}}$) when the real part of the refractive index is varied, while it synchronizes strongly and positively with $\widetilde{C}_{\text{p}}$ when the imaginary part is varied. Our analysis reveals that this duality arises from the distinct ways the real and imaginary parts of the refractive index \textbf{perturb vs.~dampen electromagnetic resonances} within the particles, leading to different coupled responses in the total scattered energy and the angular distribution. This discovery provides unprecedented insights into how phase contrast and absorption processes distinctly modulate scattering properties and the angular distribution of scattered light, particularly in regimes dominated by resonance. It establishes that the specific formulation of $\widetilde{C}_{\text{p}}$ used here is sensitive to the overall balance of multipole contributions, making it a valuable parameter for capturing refractive index-driven changes. }.

physics.optics

CPP-DIP: Multi-objective Coverage Path Planning for MAVs in Dispersed and Irregular Plantations

Coverage Path Planning (CPP) is vital in precision agriculture to improve efficiency and resource utilization. In irregular and dispersed plantations, traditional grid-based CPP often causes redundant coverage over non-vegetated areas, leading to waste and pollution. To overcome these limitations, we propose CPP-DIP, a multi-objective CPP framework designed for Micro Air Vehicles (MAVs). The framework transforms the CPP task into a Traveling Salesman Problem (TSP) and optimizes flight paths by minimizing travel distance, turning angles, and intersection counts. Unlike conventional approaches, our method does not rely on GPS-based environmental modeling. Instead, it uses aerial imagery and a Histogram of Oriented Gradients (HOG)-based approach to detect trees and extract image coordinates. A density-aware waypoint strategy is applied: Kernel Density Estimation (KDE) is used to reduce redundant waypoints in dense regions, while a greedy algorithm ensures complete coverage in sparse areas. To verify the generality of the framework, we solve the resulting TSP using three different methods: Greedy Heuristic Insertion (GHI), Ant Colony Optimization (ACO), and Monte Carlo Reinforcement Learning (MCRL). Then an object-based optimization is applied to further refine the resulting path. Additionally, CPP-DIP integrates ForaNav, our insect-inspired navigation method, for accurate tree localization and tracking. The experimental results show that MCRL offers a balanced solution, reducing the travel distance by 16.9 % compared to ACO while maintaining a similar performance to GHI. It also improves path smoothness by reducing turning angles by 28.3 % and 59.9 % relative to ACO and GHI, respectively, and effectively eliminates intersections. These results confirm the robustness and effectiveness of CPP-DIP in different TSP solvers.

cs.RO

ForaNav: Insect-inspired Online Target-oriented Navigation for MAVs in Tree Plantations

Autonomous Micro Air Vehicles (MAVs) are becoming essential in precision agriculture to enhance efficiency and reduce labor costs through targeted, real-time operations. However, existing unmanned systems often rely on GPS-based navigation, which is prone to inaccuracies in rural areas and limits flight paths to predefined routes, resulting in operational inefficiencies. To address these challenges, this paper presents ForaNav, an insect-inspired navigation strategy for autonomous navigation in plantations. The proposed method employs an enhanced Histogram of Oriented Gradient (HOG)-based tree detection approach, integrating hue-saturation histograms and global HOG feature variance with hierarchical HOG extraction to distinguish oil palm trees from visually similar objects. Inspired by insect foraging behavior, the MAV dynamically adjusts its path based on detected trees and employs a recovery mechanism to stay on course if a target is temporarily lost. We demonstrate that our detection method generalizes well to different tree types while maintaining lower CPU usage, lower temperature, and higher FPS than lightweight deep learning models, making it well-suited for real-time applications. Flight test results across diverse real-world scenarios show that the MAV successfully detects and approaches all trees without prior tree location, validating its effectiveness for agricultural automation.

cs.RO

Crystal facet orientation and temperature dependence of charge and spin Hall effects in noncollinear antiferromagnet: A first-principles investigation

Noncollinear antiferromagnets (nc-AFMs) have attracted increasing research attention in spintronics due to their unique spin structures and fascinating charge and spin transport properties. By using first-principles calculations, we comprehensively investigate the charge and spin Hall effects in representative noncollinear antiferromagnet Mn3Pt. Our study reveals that the Hall effects in nc-AFMs are critically dependent on the crystal facet orientation and temperature. For (001) orientated Mn3Pt, each charge and spin Hall conductivity element is comprised of both time reversal odd (T-odd) and even (T-even) contribution, associated with longitudinal conductivity, which leads to sizable and highly anisotropic Hall conductivity. The temperature dependence of charge and spin Hall conductivity has been elucidated by considering both phonon and spin disorder scattering. The scaling relations between Hall conductivity and longitudinal conductivity have also been investigated. The existence of prominent spin Hall effect in nc-AFMs may generate spin current with Sz spin polarization, which is advantageous for field free switching of perpendicular magnetization. Our work may provide unambiguous understanding on the charge and spin transport in noncollinear antiferromagnets and pave their way for applications in antiferromagnetic spintronics.

cond-mat.mtrl-sci

A Comprehensive Review on Tree Detection Methods Using Point Cloud and Aerial Imagery from Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) are considered cutting-edge technology with highly cost-effective and flexible usage scenarios. Although many papers have reviewed the application of UAVs in agriculture, the review of the application for tree detection is still insufficient. This paper focuses on tree detection methods applied to UAV data collected by UAVs. There are two kinds of data, the point cloud and the images, which are acquired by the Light Detection and Ranging (LiDAR) sensor and camera, respectively. Among the detection methods using point-cloud data, this paper mainly classifies these methods according to LiDAR and Digital Aerial Photography (DAP). For the detection methods using images directly, this paper reviews these methods by whether or not to use the Deep Learning (DL) method. Our review concludes and analyses the comparison and combination between the application of LiDAR-based and DAP-based point cloud data. The performance, relative merits, and application fields of the methods are also introduced. Meanwhile, this review counts the number of tree detection studies using different methods in recent years. From our statics, the detection task using DL methods on the image has become a mainstream trend as the number of DL-based detection researches increases to 45% of the total number of tree detection studies up to 2022. As a result, this review could help and guide researchers who want to carry out tree detection on specific forests and for farmers to use UAVs in managing agriculture production.

cs.CV

A review of variable-pitch propellers and their control strategies in aerospace systems

The relentless pursuit of aircraft flight efficiency has thrust variable-pitch propeller technology into the forefront of aviation innovation. This technology, rooted in the ancient power unit of propellers, has found renewed significance, particularly in the realms of unmanned aerial vehicles and urban air mobility. This underscores the profound interplay between visionary aviation concepts and the enduring utility of propellers. Variable-pitch propellers are poised to be pivotal in shaping the future of human aviation, offering benefits such as extended endurance, enhanced maneuverability, improved fuel economy, and prolonged engine life. However, with additional capabilities come new technical challenges. The development of an online adaptive control of variable-pitch propellers that does not depend on an accurate dynamic model stands as a critical imperative. Therefore, a comprehensive review and forward-looking analysis of this technology is warranted. This paper introduces the development background of variable-pitch aviation propeller technology, encompassing diverse pitch angle adjustment schemes and their integration with various engine types. It places a central focus on the latest research frontiers and emerging directions in pitch control strategies. Lastly, it delves into the research domain of constant speed pitch control, articulating the three main challenges confronting this technology: inadequacies in system modeling, the intricacies of propeller-engine compatibility, and the impact of external, time-varying factors. By shedding light on these multifaceted aspects of variable-pitch propeller technology, this paper serves as a resource for aviation professionals and researchers navigating the intricate landscape of future aircraft development.

eess.SY

Adaptive Model Predictive Control for Engine-Driven Ducted Fan Lift Systems using an Associated Linear Parameter Varying Model

Ducted fan lift systems (DFLSs) powered by two-stroke aviation piston engines present a challenging control problem due to their complex multivariable dynamics. Current controllers for these systems typically rely on proportional-integral algorithms combined with data tables, which rely on accurate models and are not adaptive to handle time-varying dynamics or system uncertainties. This paper proposes a novel adaptive model predictive control (AMPC) strategy with an associated linear parameter varying (LPV) model for controlling the engine-driven DFLS. This LPV model is derived from a global network model, which is trained off-line with data obtained from a general mean value engine model for two-stroke aviation engines. Different network models, including multi-layer perceptron, Elman, and radial basis function (RBF), are evaluated and compared in this study. The results demonstrate that the RBF model exhibits higher prediction accuracy and robustness in the DFLS application. Based on the trained RBF model, the proposed AMPC approach constructs an associated network that directly outputs the LPV model parameters as an adaptive, robust, and efficient prediction model. The efficiency of the proposed approach is demonstrated through numerical simulations of a vertical take-off thrust preparation process for the DFLS. The simulation results indicate that the proposed AMPC method can effectively control the DFLS thrust with a relative error below 3.5%.

eess.SY