arXiv subjects

Ye Zhou

Publications and source records attributed to Ye Zhou.

At least 19 recordsLinked to original sources

arXiv2026

Strict non-tightness of the level-3 quantum bootstrap for a natural three-dimensional Hamiltonian, at an excited level

For the coupled three-dimensional double well $H=\tfrac12|p|^2+2\sum_i(q_i^2-1)^2-\sum_{i<j}q_i^2q_j^2$ we exhibit, at the first excited energy level (and also at the ground state), a linear functional on polynomials of degree $\le 6$ that satisfies every constraint of the standard level-3 eigenstate bootstrap, has a \emph{strictly} positive definite moment matrix, and nevertheless assigns a negative value to a nonnegative polynomial. It is therefore an interior point of the level-3 relaxation that is realized by no quantum state. The construction reflects a fact that holds at every level: the eigenstate rows never reach the top-degree momentum moments. At level 3, where strictly feasible points exist, this means that the corresponding block of the moment matrix carries no information about which energies admit one. Every algebraic ingredient is an exact rational certificate, and the only numerical ingredients (spectral enclosures of an eigenfunction) are carried out in rigorous ball arithmetic.

quant-ph↗

arXiv2026

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 subsets spanning 1, 3, and 5 days. For the subsets spanning five days of O3 data, 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 and recovery of simulated signals demonstrate 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↗

arXiv2026

Convergence Without Finite Exactness in the Eigenstate Quantum Bootstrap

The eigenstate quantum-mechanical bootstrap can converge to the physical moment set while remaining nonexact at every finite order. We prove this for a one-dimensional quasi-exactly solvable sextic oscillator at explicitly known eigenvalues, including excited levels. At every finite order, the lower and upper bounds on $u^2$ remain strictly below and above its physical value, even after adjoining arbitrary finite polynomial Gram tests and all eigenstate-annihilator relations visible in the retained moment space. Hence neither side of the centered observable admits a finite sum-of-squares certificate modulo the eigenstate annihilator. The gap persists under any finite set of additional localizing constraints whose weights are strictly positive on polynomial vectors. We then prove convergence for polynomial Schrödinger operators satisfying explicit sum-of-squares confinement and derivative bounds. The eigenstate relations give moment estimates uniform in the truncation order; these yield a regular Schrödinger representation supported on the prescribed eigenspace. Finally, a four-mode extension combines this fixed-observable obstruction with feasible position moments having no positive representing measure at every finite order. At level three, this construction yields nonphysical feasible points with strictly positive definite moment matrices.

math-ph↗

arXiv2026

Null Completeness and Directional Rigidity in Bianchi I Spacetimes

In Einstein gravity, every null geodesic of a non-static Bianchi I spacetime satisfying the null energy condition is incomplete in at least one direction within the Bianchi I region. We prove a stronger directional rigidity theorem: a single null geodesic complete in both affine directions forces staticity if the null curvature is nonnegative along it and, at each time, in one transverse null direction. The spatial metric need not be diagonal, and the expansion rates may have either sign. The proof uses caustic-free null congruences whose conserved spatial covectors define fixed screen hyperplanes. The two curvature conditions admit averaged replacements along the geodesic and, when cosmic time is complete, along comoving worldlines. In a non-static metric, at most four oriented directions are optically saturated; every other complete null geodesic has strictly negative averaged null curvature. Periodic and finite-pulse examples attain this bound with NEC-violating sources and nonpositive total energy density. For finite pulses, we derive bounds on metric excursions from three directional curvature budgets at fixed cosmic duration, and on the mode mixing of a free, massless, minimally coupled test scalar field from the averaged null-energy deficit at fixed affine duration.

gr-qc↗

arXiv2026

Realistic modeling of Photonic Terahertz Communication based on a real-world 30 km 30 Gbps experimental demonstration

Experimental studies have demonstrated that THz links can support multi-kilometer, gigabit-per-second wireless transmission. However, few studies have effectively validated channel models through high-capacity, long-distance experiments. In this work, we experimentally demonstrate a 30 km ultra-long-haul photonic THz wireless communication system. 16 Gbaud quadrature phase shift keying (QPSK) signals are transmitted and tested, achieving a maximum data rate of 30 Gbps. For the first time, we validate two widely used THz channel models using a fully implemented 30 km experimental setup, providing a systematic and thorough analysis that integrates theoretical modeling with experimental results. We also perform an analysis of power consumption in photonic THz communication systems and provide a detailed comparison between simulated and experimental power consumption results. Finally, a detailed performance comparison between the simulation and experimental results, including signal-to-noise ratio (SNR), bit error rate (BER) and data rate, are provided. Our work provides essential insights for the design and energy-efficient deployment of future high-capacity THz networks, establishing a benchmark for subsequent research in channel modeling, experimental verification, and system-level energy efficiency.

eess.SP↗

arXiv2026

Open-channel quantization of JT gravity at finite cutoff

Direct canonical quantization of Jackiw--Teitelboim gravity at finite cutoff leads to a critically singular Pöschl--Teller problem whose endpoint admits inequivalent self-adjoint domains. We solve the full one-parameter family, obtain its exact spectral data, and identify the extension parameter with a renormalized short-distance matching datum in geodesic-length space. Self-adjointness, positivity, and unitary Brown--York evolution leave a stable family, whereas a source-free local completion without an endpoint contact term selects the Friedrichs realization. With the Brown--York clock and a local cap prescription fixed, the disk amplitude distinguishes the domains. In the large-boundary limit the bulk operator becomes universally Liouville, while a cap tied to the receding endpoint can retain domain dependence. Analytic continuation also maps radial resonances to poles of the Brown--York resolvent. These results isolate the short-distance datum that distinguishes the reduced canonical realizations and that a microscopic completion would have to determine.

gr-qc↗

arXiv2026

Attraction and repulsion along extremal Einstein--two-Maxwell--dilaton branches

We study extremal mass branches in four-dimensional Einstein gravity coupled to one dilaton and two Maxwell fields with opposite-sign dilaton couplings. For generic couplings the extremal mass is governed by a nonlinear first-order equation with no known closed-form solution. We prove that this equation has a unique positive $C^2$ solution on the full real charge-ratio line and that the solution is real analytic, so positivity on the entire positive charge cone selects a unique global mass branch. Along this branch, any two nonproportional positive charge vectors attract for $ab>-1$, have vanishing leading force at $ab=-1$, and repel for $ab<-1$. The same transition fixes the transverse convexity of the extremal mass surface and the sign of the mass difference under arbitrary finite positive-charge partitions. The sign theorem extends to higher dimensions through the known dimensional rescaling. A radial reconstruction then shows that every point of the mass branch defines an asymptotically flat exterior with a finite-area inner endpoint, while smooth or analytic horizon extension is a separate condition that restricts the couplings and scalar flow.

hep-th↗

arXiv2026

Quasinormal-mode residues and pole coalescence in hypergeometric black-hole models

We study source-normalized quasinormal-mode residues in radial boundary-value problems reducible to the Gauss hypergeometric equation. Hypergeometric connection coefficients separate the spectral condition from the normalization and spatial dependence of the frequency-domain Green function. We illustrate this construction for Dirichlet modes of the rotating Bañados--Teitelboim--Zanelli black hole and mixed boundary conditions in two-dimensional anti-de Sitter spacetime, and then apply it to the Pöschl--Teller/Nariai problem. Near pole coalescence, we obtain finite-position residues for the two simple poles, while at the critical point both coefficients of the second-order Laurent expansion are reduced to closed finite sums. The subleading coefficient depends explicitly on the frequency derivative of the Green-function numerator and is therefore not determined by the spectral function alone. We also identify the range in which the usual vanishing-discriminant criterion characterizes the Pöschl--Teller coalescence: it holds for the positive barrier considered here, whereas other double zeros or cancellations can occur outside that regime. These results provide a normalization-controlled analytic benchmark for relating spectral coalescence to sourced quasinormal response.

gr-qc↗

arXiv2026

Distance Energies and Negative Type of Flat Tori

Let $T_Λ=\mathbb R^d/Λ$, $d\geq2$, be a flat torus with quotient metric $ρ_Λ$, and consider the distance energies $I_α(μ)=\iint ρ_Λ(x,y)^α\,dμ(x)\,dμ(y)$ of Borel probability measures $μ$. We prove a quantitative Fourier signature of the cut locus: for every Voronoi facet and every $α>0$, there is a sequence of dual-lattice frequencies approaching the facet normal along which the Fourier coefficients of $ρ_Λ^α$ 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 $α=2$: for $1\leqα<2$ they are the translated uniform measures on the two-torsion subgroup; at $α=2$ all balanced couplings on translates of that subgroup are extremal; and for $α>2$ 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 $α\geq1$.

math-ph↗

arXiv2026

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 $\mathcal L_{t,κ}$ on a finite affine chart. On the $κ=0$ 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, $\mathcal L_{t,κ}$ contains a nonzero rank-one matrix if and only if $2tκ^2=1$; equivalently, these are precisely the finite-chart embeddings for which $σ_3/σ_2$ 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↗

arXiv2025

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↗

arXiv2025

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↗

arXiv2025

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↗

arXiv2025

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↗

arXiv2025

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↗

arXiv2025

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↗

arXiv2025

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↗

arXiv2025

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↗