arXiv subjects

Ning Hao

Publications and source records attributed to Ning Hao.

At least 19 recordsLinked to original sources

arXiv2026

N\'eel-Vector Control of the Josephson Diode Effect in $\mathcal{PT}$-symmetric Antiferromagnets

The interplay of superconductivity and magnetism gives rise to rich phenomena in Josephson junctions. In this Letter, we study Josephson junctions formed by conventional $s$-wave superconductors and a $\PT$-symmetric collinear antiferromagnet modeled on CuMnAs. Using microscopic modeling and symmetry analysis, we show that these junctions exhibit both the Josephson diode effect and $\varphi_{0}$-junction states. Remarkably, both effects are controlled by the N\'eel vector: rotating it by $90^{\circ}$ switches off both, while reversing it switches the diode polarity. To reveal the microscopic mechanism, we develop a channel-resolved scattering theory that accurately captures the anomalous phases and establishes the exact condition for the diode effect. The interplay of the channel current-phase relations yields a sizable diode efficiency, tunable by both the magnitude and direction of the exchange field. Furthermore, a Green-function reduction identifies a single renormalized $\PT$-degenerate band as the transport carrier and precisely reproduces the full current amplitudes. Our work establishes $\PT$-symmetric antiferromagnets as versatile platforms for field-free, highly tunable Josephson diodes and $\varphi_{0}$ junctions.

cond-mat.supr-con↗

arXiv2026

Electronic Toroidal Metals: Landau Theory and Magnetoelectric Fingerprints

Toroidal order is a higher-rank multipolar order whose intrinsic realization in itinerant-electron systems remains unexplored. Here, we develop a generic theory of the electronic toroidal metal (ETM), in which toroidal order emerges spontaneously from electronic degrees of freedom near the Fermi surfaces. Because candidate toroidal bilinears can overlap by symmetry with the uniform charge current, we formulate a projected instability criterion that removes the noncondensable current component. Within this current-orthogonal sector, we identify a soft collective mode in the $\mathcal{P}$-odd and $\mathcal{T}$-odd particle-hole channel and demonstrate that ETM arises as a Fermi-liquid instability. We then define the toroidal moment in an itinerant system through the antisymmetric magnetoelectric response tensor. We further establish an intimate connection between this response and the topology of pseudospin texture: the rearrangement of pseudospin vortices changes the winding structure of the Fermi surfaces and markedly enhances the magnetoelectric response. ETM also exhibits characteristic nonlinear charge transport, including a pronounced enhancement of its interband quantum-geometric contribution. Our results establish ETM as a distinct metallic Landau phase and provide a general framework for understanding toroidal order generated by itinerant electrons.

cond-mat.str-el↗

arXiv2026

Spectral Embeddings of Degree-$\alpha$ Laplacians in Random Dot Product Graphs

Spectral clustering methods for network data are commonly based on a few matrix representations, such as the adjacency matrix and the symmetric Laplacian. We study a continuum of degree-normalized spectral embeddings that includes these commonly used choices as special cases. Under a random dot product graph model, we establish a row-wise central limit theorem for this family of embeddings. The result provides an explicit description of how degree normalization affects both population geometry and the local uncertainty of embedded nodes. We use the limiting distributions to compare different normalizations in two-community stochastic block models through a projected-Gaussian Bayes-error diagnostic. These comparisons show that no single normalization is uniformly preferred. Instead, the favored normalization depends on network density, community imbalance, and block-probability structure. Typically, stronger normalization is favored in lower-density or more imbalanced settings. These results provide a unified distributional understanding of when and why alternative normalizations may improve spectral clustering.

stat.ML↗

arXiv2026

A Unified Symmetry Framework For In-plane Anomalous Hall effect

The in-plane anomalous Hall effect (IPAHE), driven by an in-plane net magnetization or an applied magnetic field, challenges the conventional anomalous Hall paradigm. Despite growing interest, a unified symmetry principle governing these phenomena has remained elusive. Here, we establish a comprehensive symmetry framework that bridges the spin space group, which dictates the magnetic geometry, with the magnetic space group, which governs the anomalous Hall response. We show that spontaneous IPAHE can emerge in ferromagnets when spin-orbit-coupling-induced spin-group symmetry breaking permits additional net magnetization directions. For field-induced IPAHE, we analyze how an applied magnetic field reduces the symmetries of all 122 magnetic point groups and identify 54 groups that support IPAHE. Our framework naturally predicts IPAHE in a broad class of unconventional magnets, including altermagnets and odd-parity magnets. In particular, symmetry analysis reveals characteristic one-, two-, or three-fold angular harmonics of the Hall conductance under an in-plane rotating field, providing a symmetry-resolved fingerprint for unconventional magnetism. Using this framework, we screen the MAGNDATA database and identify candidate materials supporting spontaneous or field-induced IPAHE, encompassing ferromagnets, antiferromagnets, and unconventional magnets. Finally, we validate the symmetry predictions through first-principles calculations for two representative materials.

cond-mat.mtrl-sci↗

arXiv2026

Equivariant Filter Transformations for Consistent and Efficient Visual--Inertial Navigation

This paper presents an equivariant filter (EqF) transformation approach for visual--inertial navigation. By establishing analytical links between EqFs with different symmetries, the proposed approach enables systematic consistency design and efficient implementation. First, we formalize the mapping from the global system state to the local error-state and prove that it induces a nonsingular linear transformation between the error-states of any two EqFs. Second, we derive transformation laws for the associated linearized error-state systems and unobservable subspaces. These results yield a general consistency design principle: for any unobservable system, a consistent EqF with a state-independent unobservable subspace can be synthesized by transforming the local coordinate chart, thereby avoiding ad hoc symmetry analysis. Third, to mitigate the computational burden arising from the non-block-diagonal Jacobians required for consistency, we propose two efficient implementation strategies. These strategies exploit the Jacobians of a simpler EqF with block-diagonal structure to accelerate covariance operations while preserving consistency. Extensive Monte Carlo simulations and real-world experiments validate the proposed approach in terms of both accuracy and runtime.

cs.RO↗

arXiv2026

Ellipticity-Controlled Bright-Dark Coherence Transition in Monolayer WSe2

The generation of exciton valley coherence typically requires linearly polarized (LP) light as an external coherent drive, whereas circularly polarized (CP) light fails to induce coherence. Here, we develop a unified, microscopically-grounded open-quantum-system framework within a five-level model incorporating bright-dark exciton interactions in monolayer WSe2, and demonstrate that the polarization ellipticity of the excitation field provides selective control over distinct exciton species contributing to valley coherence. Specifically, LP and CP excitations generate bright and dark coherence, respectively, with continuous ellipticity tuning enabling controlled transitions between these states. We further reveal dual magnetic advantages for manipulating dark coherence even in the absence of initial coherence: (i) an out-of-plane magnetic field suppresses coherence decay and (ii) an in-plane field enables its optical readout, with quantitatively realistic field strengths. These findings provide a powerful mechanism for accessing hidden dark states via ellipticity-driven coherence transfer, and establish a new pathway for harnessing bright-dark valley-coherence transitions in future quantum control.

cond-mat.mes-hall↗

arXiv2026

Higher-Order Topological Superconductivity and Electrically Tunable Majorana Corner Modes in Monolayer MnXPb$_2$ (X=Se, Te)-Pb Heterostructure

Higher-order topological superconductors host Majorana zero modes localized at corners or hinges, providing a promising route toward scalable and controllable Majorana networks without vortices or magnetic flux. Here we propose a symmetry-enforced higher-order topological superconductivity based on antiferromagnetic topological insulators, specifically realized in MnXPb$_2$ (X = Se, Te)-Pb heterostructure. We show that the intrinsic boundary dichotomy-gapless Dirac states protected by an effective time-reversal symmetry on antiferromagnetic edges and magnetic gaps on ferromagnetic edges-naturally generates Majorana corner modes as mass domain walls. Superconducting proximity converts the antiferromagnetic edges into one-dimensional topological superconductors, and the intersections between superconducting and magnetic edges bind Majorana zero modes as mass domain walls. Combining first-principles calculations with a calibrated effective boundary theory, we demonstrate robust corner localization and purely electrical control of Majorana fusion and braiding in a triangular geometry. Our results establish MnXPb$_2$ as experimentally promising platform for electrically programmable Majorana networks in two dimensions.

cond-mat.supr-con↗

arXiv2026

CLIDD: Cross-Layer Independent Deformable Description for Efficient and Discriminative Local Feature Representation

Robust local feature representations are essential for spatial intelligence tasks such as robot navigation and augmented reality. Establishing reliable correspondences requires descriptors that provide both high discriminative power and computational efficiency. To address this, we introduce Cross-Layer Independent Deformable Description (CLIDD), a method that achieves superior distinctiveness by sampling directly from independent feature hierarchies. This approach utilizes learnable offsets to capture fine-grained structural details across scales while bypassing the computational burden of unified dense representations. To ensure real-time performance, we implement a hardware-aware kernel fusion strategy that maximizes inference throughput. Furthermore, we develop a scalable framework that integrates lightweight architectures with a training protocol leveraging both metric learning and knowledge distillation. This scheme generates a wide spectrum of model variants optimized for diverse deployment constraints. Extensive evaluations demonstrate that our approach achieves superior matching accuracy and exceptional computational efficiency simultaneously. Specifically, the ultra-compact variant matches the precision of SuperPoint while utilizing only 0.004M parameters, achieving a 99.7% reduction in model size. Furthermore, our high-performance configuration outperforms all current state-of-the-art methods, including high-capacity DINOv2-based frameworks, while exceeding 200 FPS on edge devices. These results demonstrate that CLIDD delivers high-precision local feature matching with minimal computational overhead, providing a robust and scalable solution for real-time spatial intelligence tasks.

cs.CV↗

arXiv2026

Strain-Driven "Sinusoidal" Valley Control of Hybridized $\Gamma -\mathrm{K}$ Excitons

The photoluminescence (PL) of momentum-indirect $\rm \Gamma- K$ excitons in monolayer WS$_2$ under biaxial strain was recently observed by Blundo et al. [Phys. Rev. Lett. 129, 067402 (2022)], yet its microscopic origin remains elusive. Here we develop a unified framework that reproduces the measured PL and reveals its fundamental excitonic mechanism. We reveal that: (i) the PL originates from genuinely hybridized direct-indirect excitonic eigenstates, rather than nominally mixed species with fixed dominant character; (ii) the direct exciton converts into the indirect one via a previously unrecognized two-step pathway -- exchange-interaction-driven exciton transfer followed by a spin flip; and (iii) a higher-energy indirect exciton, absent from prior studies, acts as a crucial intermediate mediating this conversion. Beyond explaining experiment, our theory predicts a striking strain-driven "sinusoidal'' valley response, furnishing a continuously tunable valley dial that far exceeds binary control schemes. This unified picture of strain-engineered direct-indirect exciton dynamics introduces a new paradigm for manipulating long-lived valley degrees of freedom, opening a pathway toward programmable valley pseudospin engineering and next-generation valleytronic quantum technologies.

cond-mat.mes-hall↗

arXiv2025

Unobservable Subspace Evolution and Alignment for Consistent Visual-Inertial Navigation

The inconsistency issue in the Visual-Inertial Navigation System (VINS) is a long-standing and fundamental challenge. While existing studies primarily attribute the inconsistency to observability mismatch, these analyses are often based on simplified theoretical formulations that consider only prediction and SLAM correction. Such formulations fail to cover the non-standard estimation steps, such as MSCKF correction and delayed initialization, which are critical for practical VINS estimators. Furthermore, the lack of a comprehensive understanding of how inconsistency dynamically emerges across estimation steps has hindered the development of precise and efficient solutions. As a result, current approaches often face a trade-off between estimator accuracy, consistency, and implementation complexity. To address these limitations, this paper proposes a novel analysis framework termed Unobservable Subspace Evolution (USE), which systematically characterizes how the unobservable subspace evolves throughout the entire estimation pipeline by explicitly tracking changes in its evaluation points. This perspective sheds new light on how individual estimation steps contribute to inconsistency. Our analysis reveals that observability misalignment induced by certain steps is the antecedent of observability mismatch. Guided by this insight, we propose a simple yet effective solution paradigm, Unobservable Subspace Alignment (USA), which eliminates inconsistency by selectively intervening only in those estimation steps that induce misalignment. We design two USA methods: transformation-based and re-evaluation-based, both offering accurate and computationally lightweight solutions. Extensive simulations and real-world experiments validate the effectiveness of the proposed methods.

cs.RO↗

arXiv2025

An Unusual Dresselhaus Spin-Orbit Contribution of Even Order in Momentum

The spin-orbit (SO) coupling is conventionally known to manifest as \emph{odd} functions of momentum. Here, through both model calculations and symmetry analysis along with the method of invariants, we reveal that, in ordinary semiconductor heterostructures, a \emph{quadratic} Dresselhaus SO term -- inheriting from its bulk crystal form -- emerges via the interband effect, while complying with time-reversal and spatial symmetries. Furthermore, we observe that this unusual SO term gives rise to a range of striking quantum phenomena, including hybridized swirling texture, anisotropic energy dispersion, avoided band crossing, longitudinal \emph{Zitterbewegung}, and opposite spin evolution between different bands in quantum dynamics. These stand in stark contrast to those associated with the usual \emph{linear} SO terms. Our findings uncover a previously overlooked route for exploiting interband effects and open new avenues for spintronic functionalities that leverage unusual SO terms of \emph{even} orders in momentum.

cond-mat.mes-hall↗

arXiv2025

T-ESKF: Transformed Error-State Kalman Filter for Consistent Visual-Inertial Navigation

This paper presents a novel approach to address the inconsistency problem caused by observability mismatch in visual-inertial navigation systems (VINS). The key idea involves applying a linear time-varying transformation to the error-state within the Error-State Kalman Filter (ESKF). This transformation ensures that \textrr{the unobservable subspace of the transformed error-state system} becomes independent of the state, thereby preserving the correct observability of the transformed system against variations in linearization points. We introduce the Transformed ESKF (T-ESKF), a consistent VINS estimator that performs state estimation using the transformed error-state system. Furthermore, we develop an efficient propagation technique to accelerate the covariance propagation based on the transformation relationship between the transition and accumulated matrices of T-ESKF and ESKF. We validate the proposed method through extensive simulations and experiments, demonstrating better (or competitive at least) performance compared to state-of-the-art methods. The code is available at github.com/HITCSC/T-ESKF.

cs.RO↗

arXiv2025

Autocorrelation Test under Frequent Mean Shifts

Testing for the presence of autocorrelation is a fundamental problem in time series analysis. Classical methods such as the Box-Pierce test rely on the assumption of stationarity, necessitating the removal of non-stationary components such as trends or shifts in the mean prior to application. However, this is not always practical, particularly when the mean structure is complex, such as being piecewise constant with frequent shifts. In this work, we propose a new inferential framework for autocorrelation in time series data under frequent mean shifts. In particular, we introduce a Shift-Immune Portmanteau (SIP) test that reliably tests for autocorrelation and is robust against mean shifts. We illustrate an application of our method to nanopore sequencing data.

stat.ME↗

arXiv2025

Interpretable Scalar-on-Image Linear Regression Models via the Generalized Dantzig Selector

The scalar-on-image regression model examines the association between a scalar response and a bivariate function (e.g., images) through the estimation of a bivariate coefficient function. Existing approaches often impose smoothness constraints to control the bias-variance trade-off, and thus prevent overfitting. However, such assumptions can hinder interpretability, especially when only certain regions of an image influence changes in the response. In such a scenario, interpretability can be better captured by imposing sparsity assumptions on the coefficient function. To address this challenge, we propose the Generalized Dantzig Selector, a novel method that jointly enforces sparsity and smoothness on the coefficient function. The proposed approach enhances interpretability by accurately identifying regions with no contribution to the changes of response, while preserving stability in estimation. Extensive simulation studies and real data applications demonstrate that the new method is highly interpretable and achieves notable improvements over existing approaches. Moreover, we rigorously establish non-asymptotic bounds for the estimation error, providing strong theoretical guarantees for the proposed framework.

stat.ME↗

arXiv2025

Algebraic Criterion and Graph-Theoretic Construction of Intrinsic Superconducting Diode Effects

The intrinsic superconducting diode effect (SDE) is distinguished from the Josephson diode effect (JDE) by its manifestation of nonreciprocal critical current phenomena within a monolithic superconductor, typically linked to finite-momentum Cooper pairing. The long-standing assumption that SDE requires co-breaking of time-reversal and inversion symmetries proves to be necessary but not sufficient. In this work, we propose a universal diagnostic criterion for intrinsic SDE, expressed as two inequalities evaluated directly from the bare Hamiltonian. This criterion further reveals a graph-theoretic construction for nonreciprocal models, offering design principles that extend beyond superconductivity.

cond-mat.supr-con↗

arXiv2025

Multi-gap and high-Tc superconductivity in metal-atom-free borocarbides: Effects of dimensional confinement and strain engineering

Pure borocarbides suffer from limited superconducting potential due to intrinsic structural instability, requiring transition/alkali metals as dual-functional stabilizers and dopants. Here, by combining high-throughput screening with anisotropic Migdal-Eliashberg (aME) theory, we identify dynamically stable borocarbides where high-Tc superconductivity predominately originates from E symmetry-selective electron-phonon coupling (EPC). The six distinct superconducting gaps emerge from a staircase distribution or uncoupling of EPC strength across each Fermi surface (FS) sheet, constituting a metal-free system with such high gap multiplicity. Crucially, dimensional reduction from bulk to surface strengthens E-symmetry EPC and enhances Tc from 32 K (3D bulk) to 75 K (2D surface), a result that highlights structural confinement as a key design strategy for observing high Tc. External strain further optimizes the competition between EPC strength and characteristic phonon frequency to achieve Tc > 90 K. This work reveals a systematic correlation between structural dimensionality and gap multiplicity and establishes borocarbide as a tunable platform to engineer both high-Tc and multi-gap superconductivity.

cond-mat.supr-con↗

arXiv2025

Kernel Density Balancing

High-throughput chromatin conformation capture (Hi-C) data provide insights into the 3D structure of chromosomes, with normalization being a crucial pre-processing step. A common technique for normalization is matrix balancing, which rescales rows and columns of a Hi-C matrix to equalize their sums. Despite its popularity and convenience, matrix balancing lacks statistical justification. In this paper, we introduce a statistical model to analyze matrix balancing methods and propose a kernel-based estimator that leverages spatial structure. Under mild assumptions, we demonstrate that the kernel-based method is consistent, converges faster, and is more robust to data sparsity compared to existing approaches.

stat.AP↗

arXiv2025

EdgePoint2: Compact Descriptors for Superior Efficiency and Accuracy

The field of keypoint extraction, which is essential for vision applications like Structure from Motion (SfM) and Simultaneous Localization and Mapping (SLAM), has evolved from relying on handcrafted methods to leveraging deep learning techniques. While deep learning approaches have significantly improved performance, they often incur substantial computational costs, limiting their deployment in real-time edge applications. Efforts to create lightweight neural networks have seen some success, yet they often result in trade-offs between efficiency and accuracy. Additionally, the high-dimensional descriptors generated by these networks poses challenges for distributed applications requiring efficient communication and coordination, highlighting the need for compact yet competitively accurate descriptors. In this paper, we present EdgePoint2, a series of lightweight keypoint detection and description neural networks specifically tailored for edge computing applications on embedded system. The network architecture is optimized for efficiency without sacrificing accuracy. To train compact descriptors, we introduce a combination of Orthogonal Procrustes loss and similarity loss, which can serve as a general approach for hypersphere embedding distillation tasks. Additionally, we offer 14 sub-models to satisfy diverse application requirements. Our experiments demonstrate that EdgePoint2 consistently achieves state-of-the-art (SOTA) accuracy and efficiency across various challenging scenarios while employing lower-dimensional descriptors (32/48/64). Beyond its accuracy, EdgePoint2 offers significant advantages in flexibility, robustness, and versatility. Consequently, EdgePoint2 emerges as a highly competitive option for visual tasks, especially in contexts demanding adaptability to diverse computational and communication constraints.

cs.CV↗