arXiv subjects

Ning Hao

Publications and source records attributed to Ning Hao.

At least 37 records · Page 2Linked to original sources

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

A Note on the Identifiability of the Degree-Corrected Stochastic Block Model

In this short note, we address the identifiability issues inherent in the Degree-Corrected Stochastic Block Model (DCSBM). We provide a rigorous proof demonstrating that the parameters of the DCSBM are identifiable up to a scaling factor and a permutation of the community labels, under a mild condition.

stat.ME↗

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↗

arXiv2025

Finite-Momentum Pairing State in Unconventional Rashba Systems

In systems with unconventional Rashba bands, we propose that a finite-momentum pairing state can emerge without the need for an external magnetic field. We analyze the phase transition from a zero-momentum Bardeen-Cooper-Schrieffer (BCS) state to a finite-momentum pairing state using a microscopic interaction model. We demonstrate that the coexistence of zero-momentum and Larkin-Ovchinnikov (LO)-type pairings in different channels provides a well understanding of recent experimental observations of pair-density-wave (PDW) states. Furthermore, we propose that an s-wave BCS superconductor-unconventional Rashba metal (SC-URM) junction can generate an LO-type pairing state via an orbital-selective proximity effect. This nontrivial state can be detected by measuring the Josephson current in an SC-URM-SC junction or through Josephson scanning tunneling microscopy/spectrosscopy (JSTM/S). Our results reveal that the internal multi-orbital degrees of freedom play a crucial role in facilitating finite-momentum pairing states.

cond-mat.supr-con↗

arXiv2025

Symmetry-Broken Kondo Screening and Zero-Energy Mode in the Kagome Superconductor CsV3Sb5

The quantum states of matter reorganize themselves in response to defects, giving rise to emergent local excitations that imprint unique characteristics of the host states. While magnetic impurities are known to generate Kondo screening in a Fermi liquid and Yu-Shiba-Rusinov (YSR) states in a conventional superconductor, it remains unclear whether they can evoke distinct phenomena in the kagome superconductor AV3Sb5 (where A is K, Rb or Cs), which may host an orbital-antiferromagnetic charge density wave (CDW) state and an unconventional superconducting state driven by the convergence of topology, geometric frustration and electron correlations. In this work, we visualize the local density of states induced near various types of impurities in both the CDW and superconducting phases of CsV3-xMxSb5 (M = Ta, Cr) using scanning tunneling microscopy. We observe Kondo resonance states near magnetic Cr dopants. Notably, unlike in any known metal or CDW compound, the spatial pattern of Kondo screening breaks all in-plane mirror symmetries of the kagome lattice, suggesting an electronic chirality due to putative orbital loop currents. While Cooper pairs show relative insensitivity to nonmagnetic impurities, native V vacancies with weak magnetic moments induce a pronounced zero-bias conductance peak (ZBCP). This ZBCP coexists with trivial YSR states within the superconducting gap and does not split in energy with increasing tunneling transmission, tending instead to saturate. This behavior is reminiscent of signature of Majorana zero modes, which could be trapped by a sign-change boundary in the superconducting order parameter near a V vacancy, consistent with a surface topological superconducting state. Our findings provide a new approach to exploring novel quantum states on kagome lattices.

cond-mat.supr-con↗

arXiv2025

Dynamic Supervised Principal Component Analysis for Classification

This paper introduces a novel framework for dynamic classification in high dimensional spaces, addressing the evolving nature of class distributions over time or other index variables. Traditional discriminant analysis techniques are adapted to learn dynamic decision rules with respect to the index variable. In particular, we propose and study a new supervised dimension reduction method employing kernel smoothing to identify the optimal subspace, and provide a comprehensive examination of this approach for both linear discriminant analysis and quadratic discriminant analysis. We illustrate the effectiveness of the proposed methods through numerical simulations and real data examples. The results show considerable improvements in classification accuracy and computational efficiency. This work contributes to the field by offering a robust and adaptive solution to the challenges of scalability and non-staticity in high-dimensional data classification.

stat.ME↗

arXiv2025

A Transformation-based Consistent Estimation Framework: Analysis, Design and Applications

In this paper, we investigate the inconsistency problem arising from observability mismatch that frequently occurs in nonlinear systems such as multi-robot cooperative localization and simultaneous localization and mapping. For a general nonlinear system, we discover and theoretically prove that the unobservable subspace of the EKF estimator system is independent of the state and belongs to the unobservable subspace of the original system. On this basis, we establish the necessary and sufficient conditions for achieving observability matching. These theoretical findings motivate us to introduce a linear time-varying transformation to achieve a transformed system possessing a state-independent unobservable subspace. We prove the existence of such transformations and propose two design methodologies for constructing them. Moreover, we propose two equivalent consistent transformation-based EKF estimators, referred to as T-EKF 1 and T-EKF 2, respectively. T-EKF 1 employs the transformed system for consistent estimation, whereas T-EKF 2 leverages the original system but ensures consistency through state and covariance corrections from transformations. To validate our proposed methods, we conduct experiments on several representative examples, including multi-robot cooperative localization, multi-source target tracking, and 3D visual-inertial odometry, demonstrating that our approach achieves state-of-the-art performance in terms of accuracy, consistency, computational efficiency, and practical realizations.

cs.RO↗

arXiv2024

Community Detection with Heterogeneous Block Covariance Model

Community detection is the task of clustering objects based on their pairwise relationships. Most of the model-based community detection methods, such as the stochastic block model and its variants, are designed for networks with binary (yes/no) edges. In many practical scenarios, edges often possess continuous weights, spanning positive and negative values, which reflect varying levels of connectivity. To address this challenge, we introduce the heterogeneous block covariance model (HBCM) that defines a community structure within the covariance matrix, where edges have signed and continuous weights. Furthermore, it takes into account the heterogeneity of objects when forming connections with other objects within a community. A novel variational expectation-maximization algorithm is proposed to estimate the group membership. The HBCM provides provable consistent estimates of memberships, and its promising performance is observed in numerical simulations with different setups. The model is applied to a single-cell RNA-seq dataset of a mouse embryo and a stock price dataset. Supplementary materials for this article are available online.

stat.ML↗

arXiv2024

Consistent Distributed Cooperative Localization: A Coordinate Transformation Approach

This paper addresses the consistency issue of multi-robot distributed cooperative localization. We introduce a consistent distributed cooperative localization algorithm conducting state estimation in a transformed coordinate. The core idea involves a linear time-varying coordinated transformation to render the propagation Jacobian independent of the state and make it suitable for a distributed manner. This transformation is seamlessly integrated into a server-based distributed cooperative localization framework, in which each robot estimates its own state while the server maintains the cross-correlations. The transformation ensures the correct observability property of the entire framework. Moreover, the algorithm accommodates various types of robot-to-robot relative measurements, broadening its applicability. Through simulations and real-world dataset experiments, the proposed algorithm has demonstrated better performance in terms of both consistency and accuracy compared to existing algorithms.

cs.RO↗

arXiv2024

Superconductivity in Two-Dimensional Systems with Unconventional Rashba Bands

In two-dimensional system with Rashba spin-orbit coupling, it is well-known that superconductivity can have mixed spin-singlet and -triplet parity, and the $\boldsymbol{d}$-vector of spin-triplet pairing is parallel to $\boldsymbol{g}$-vector of Rashba spin-orbit coupling. Here, we propose a model to describe a two-dimensional system with unconventional Rashba bands and study its superconductivity. We show that the $\boldsymbol{d}$-vector of spin-triplet pairing can be either parallel or perpendicular to $\boldsymbol{g}$-vector of Rashba spin-orbit coupling depending on the different pairing interaction. We also propose a junction to generate tunneling current depending on the direction of $\boldsymbol{d}$-vector. It provides a detectable evidence to distinguish these two different but very similar pairing channels. Furthermore, we find this model can give arise to a subleading spin-singlet chiral $p$-wave topological superconducting state. More significantly, we find that such unconventional Rashba bands and unconventional superconudcting pairings can be realized on surface of some superconducting topological materials, such as trigonal layered PtBi$_{2}$.

cond-mat.supr-con↗

arXiv2024

Quantum Geometric Effects on the Higgs Mode in Flat-band Superconductors

Flat-band systems are of great interest due to their strong electron correlations and unique band geometry. Recent studies have linked the properties of Cooper pairs in flat-band superconductors to the quantum metric. Unlike prior studies primarily focused on quasiparticle fluctuations, in this work, we investigate quantum geometric effects on a collective mode of the order parameter-the Higgs mode. We derive the quantum goemetric Higgs-mode correlation length and investigate whether the nonlinear electromagnetic response of the Higgs mode persists in flat band. It turns out that the quantum metric will replaces energy derivatives, playing a crucial role in third-harmonic generation(THG). We further numerically calculated the correlation length and THG in the Lieb lattice. In contrast to traditional single-band results, Higgs mode fluctuations contribute almost entirely to THG, with the quasiparticle contribution being negligible. This finding provides valuable guidance for detecting Higgs modes in flat-band systems through optical methods and reveals the profound influence of quantum geometry in flat-band systems.

cond-mat.supr-con↗

arXiv2024

Generalized Topology in Lattice Models without Chiral Symmetry

The Su-Schrieffer-Heeger (SSH) model is a fundamental lattice model used to study topological physics. Here, we propose a new versatile one-dimensional (1D) lattice model that extends beyond the SSH model. Our 1D model breaks chiral symmetry and has generalized topology characterized by a projected winding number $W_{1D,P}=1$. When this model is extended to 2D, it can generate a second-order topological insulator (SOTI) phase. The generalized topology of the SOTI phase is protected by a pair of opposite winding numbers $W_{2D,P}^{\pm}=\pm1$, which count the opposite phase windings of a projected vortex and antivortex pair defined in the manifold of the entire parameter space. Thus, the topology of our models is robust and the end (corner) modes are independent of the selection of unit cells and boundary configurations. More significantly, we demonstrate that the model is very general and can be inherently realized in many categories of crystalline materials such as BaHCl.

cond-mat.mes-hall↗

arXiv2024

Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks

Bipartite graphs are ubiquitous across various scientific and engineering fields. Simultaneously grouping the two types of nodes in a bipartite graph via biclustering represents a fundamental challenge in network analysis for such graphs. The latent block model (LBM) is a commonly used model-based tool for biclustering. However, the effectiveness of the LBM is often limited by the influence of row and column sums in the data matrix. To address this limitation, we introduce the degree-corrected latent block model (DC-LBM), which accounts for the varying degrees in row and column clusters, significantly enhancing performance on real-world data sets and simulated data. We develop an efficient variational expectation-maximization algorithm by creating closed-form solutions for parameter estimates in the M steps. Furthermore, we prove the label consistency and the rate of convergence of the variational estimator under the DC-LBM, allowing the expected graph density to approach zero as long as the average expected degrees of rows and columns approach infinity when the size of the graph increases.

stat.ML↗

arXiv2024

Discovery of smectic charge and pair-density-wave orders in topological monolayer 1T$^\prime$-MoTe$_2$

Electronic liquid-crystal phases are observed in numerous strongly-correlated systems including high-temperature superconductors. However, identifying these exotic phases and understanding their interplay with superconductivity in topological materials remain challenging. Here we employ a cryogenic scanning tunneling microscopy to discover a smectic (stripe) charge order (CO) and a primary pair-density-wave (PDW) in topological monolayer 1T$^\prime$-MoTe$_2$. The two orders are spatially modulated unidirectionally at the same wavevector, but have a marked spatial phase difference of about 2$π$/5. Importantly, the primary PDW state features a two-gap superconductivity below the transition temperature of 6.0 K and induces another unique particle-hole-symmetric CO at twice the PDW wavevector. Combining these results and our density functional calculations, we reveal that the two smectic orders are primarily driven by nesting behaviors between electron and hole pockets. Our findings establish monolayer 1T$^\prime$-MoTe$_2$ as a topological paradigm for exploring electronic smecticity, which intertwines with multiple preexisting symmetry-breaking states.

cond-mat.supr-con↗

arXiv2024

TopoTB: A software package for calculating the electronic structure and topological properties of the tight-binding model

We present TopoTB, a software package written in the Mathematica language, designed to compute electronic structures, topological properties, and phase diagrams based on tight-binding models. TopoTB is user-friendly, with an interactive user interface that enables the tuning of model parameters for fitting the target energy bands in a WYSIWYG way. In addition, TopoTB also includes functionalities for processing results from Density Functional Theory calculations. The outputs of TopoTB are rich and readable, and they can be displayed in various styles. These features make TopoTB a useful tool for the theoretical study of materials.

cond-mat.mtrl-sci↗

arXiv2024

A generic model with unconventional Rashba bands and giant spin galvanic effect

In two-dimensional system, Rashba spin-orbit coupling can lift spin degeneracy and gives the opposite spin chirality of two split Fermi circles from two Rashba bands. Here, we propose a generic model which can produce unconventional Rashba bands. In such a case, the two Fermi circles from two bands have the same spin chirality. When various interactions are taken into account, many unique physics can emerge in case of unconventional Rashba bands in comparison with in case of conventional Rashba bands. For instance, we study the spin galvanic effect by considering two cases with potential impurity scattering and magnetic impurity scattering, respectively. In both cases, we find the efficiency of spin galvanic effect is strongly enhanced in unconventional Rashba bands in comparison with conventional Rashba bands. More intriguingly, we find the effeiciency of conventional Rashba bands is insensitive to potential or magnetic impurity scattering. However, such efficiency of uncoventional Rashba bands can be further enhanced by the magnetic impurity scattering in comparison with the potential impurity scattering. Thus, the unconventional Rashba bands can give giant spin galvanic effect. These results show that this model is useful to explore abnormal physics in the systems with unconventional Rashba bands.

cond-mat.mes-hall↗

arXiv2023

Equivariant Variance Estimation for Multiple Change-point Model

The variance of noise plays an important role in many change-point detection procedures and the associated inferences. Most commonly used variance estimators require strong assumptions on the true mean structure or normality of the error distribution, which may not hold in applications. More importantly, the qualities of these estimators have not been discussed systematically in the literature. In this paper, we introduce a framework of equivariant variance estimation for multiple change-point models. In particular, we characterize the set of all equivariant unbiased quadratic variance estimators for a family of change-point model classes, and develop a minimax theory for such estimators.

stat.ME↗