arXiv subjects

Jianhua Zhao

Publications and source records attributed to Jianhua Zhao.

At least 19 recordsLinked to original sources

arXiv2026

Interpretable Multi-Hypersphere Deep Anomaly Detection for Open-set Supervised Anomaly Detection

Multi-class open-set anomaly detection requires a model to characterize the normal acceptance domain formed by multiple heterogeneous subdistributions using only class-labeled samples from known normal classes, and to identify previously unseen anomalies at test time. Existing single-hypersphere methods cannot explicitly represent class-specific locations and acceptance ranges, while current multi-hypersphere or multi-class approaches do not fully integrate inter-class boundary constraints, learnable acceptance ranges, and interpretable decisions. To address these limitations, we propose Interpretable Multi-Hypersphere Deep Anomaly Detection (IMHD-AD). IMHD-AD constructs an independent hypersphere for each known normal class in a shared feature space. With target-inside and non-target-outside constraints, IMHD-AD embeds the class-specific hypersphere centers and radii directly into the final network layer and jointly optimizes them with the shared representation. The minimum signed boundary score across hyperspheres simultaneously determines open-set acceptance or rejection and provides a faithful geometric explanation of each decision. On MNIST, Fashion-MNIST, and CIFAR-10, IMHD-AD achieves the highest AUC in 28 of 30 open-set comparisons. A two-dimensional synthetic study further shows that model architecture must balance the compactness of known normal classes against the separability of unknown anomalies.

cs.LG↗

arXiv2026

Interpretable Maximum Margin Deep Anomaly Detection

Anomaly detection is a crucial machine-learning task with wide-ranging applications. Deep Support Vector Data Description (Deep SVDD) is a prominent deep one-class method, but it is vulnerable to hypersphere collapse, often relies on heuristic choices for hypersphere parameters, and provides limited interpretability. To address these issues, we propose Interpretable Maximum Margin Deep Anomaly Detection (IMD-AD), which leverages a small set of labeled anomalies and a maximum margin objective to stabilize training and improve discrimination. It is inherently resilient to hypersphere collapse. Furthermore, we prove an equivalence between hypersphere parameters and the network's final-layer weights, which allows the center and radius to be learned end-to-end as part of the model and yields intrinsic interpretability and visualizable outputs. We further develop an efficient training algorithm that jointly optimizes representation, margin, and final-layer parameters. Extensive experiments and ablation studies on image and tabular benchmarks demonstrate that IMD-AD empirically improves detection performance over several state-of-the-art baselines while providing interpretable decision diagnostics.

cs.LG↗

arXiv2026

Absence of Orbital Hall Magnetoresistance in Nonmagnet/Ferromagnet Bilayers with Large Orbital Torque

We report the absence of orbital Hall magnetoresistance (OMR) in nonmagnet/ferromagnet bilayers, challenging the general assumption that orbital transport mimics spin transport. Despite the observation of giant orbital torques, confirming the generation of orbital currents, thickness-dependent magnetoresistance measurements reveal that the signal is dominated by the intrinsic magnetoresistance of the ferromagnet and current shunting, with no discernible OMR contribution. We attribute this contradiction to the distinct transport properties of orbital compared with spin. Orbital currents undergo isotropic bulk absorption in the ferromagnet rather than anisotropic interfacial reflection required for OMR. Furthermore, we find that texture-induced magnetoresistance and self-torques in Ni-based bilayers can generate misleading signals, suggesting that caution is required when employing Ni in orbitronic studies. These findings clarify the distinct physical rules governing orbital transport and provide a simple method to distinguish spin and orbital currents.

cond-mat.mes-hall↗

arXiv2026

Spin splitting, Kondo correlation and singlet-doublet quantum phase transition in a superconductor-coupled InSb nanosheet quantum dot

We realize a superconductor-coupled quantum dot (QD) in an InSb nanosheet, a 2D platform promising for studies of topological superconductivity. The device consists of a superconductor-QD-superconductor junction, where a bottom bilayer gate defines the QD and allows tuning of its coupling to the superconducting leads. The QD exhibits large $g$-factors and strong spin-orbit coupling. Transport measurements reveal Coulomb diamond-shaped differential conductance features with even-odd alternating sizes and pronounced conductance lines associated with the superconducting gap, confirming a few-electron, superconductor-coupled regime. At an odd electron occupation, Kondo signatures emerge, including a zero-bias peak that splits with magnetic field and is logarithmically suppressed at elevated temperatures. We further observe a doublet-singlet quantum phase transition, manifested by a clear change of Andreev bound states from crossing to anticrossing as the coupling strength increases. These results underscore the rich physics of InSb nanosheet QDs and their promise for topological quantum devices.

cond-mat.supr-con↗

arXiv2026

Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection

Graph anomaly detection (GAD) has garnered increasing attention in recent years, yet remains challenging due to two key factors: (1) label scarcity stemming from the high cost of annotations and (2) homophily disparity at node and class levels. In this paper, we introduce Anomaly-Aware Pre-Training and Fine-Tuning (APF), a targeted and effective framework to mitigate the above challenges in GAD. In the pre-training stage, APF incorporates node-specific subgraphs selected via the Rayleigh Quotient, a label-free anomaly metric, into the learning objective to enhance anomaly awareness. It further introduces two learnable spectral polynomial filters to jointly learn dual representations that capture both general semantics and subtle anomaly cues. During fine-tuning, a gated fusion mechanism adaptively integrates pre-trained representations across nodes and dimensions, while an anomaly-aware regularization loss encourages abnormal nodes to preserve more anomaly-relevant information. Furthermore, we theoretically show that APF tends to achieve linear separability under mild conditions. Comprehensive experiments on 10 benchmark datasets validate the superior performance of APF in comparison to state-of-the-art baselines.

cs.LG↗

arXiv2026

Deterministic non-local parity control and supercurrent-based detection in an Andreev molecule

The ability to manipulate and detect the parity of quantum states in superconductor-semiconductor hybrid systems is pivotal to realizing the promise of topological quantum computation. However, as these architectures scale toward artificial Kitaev chains with phase-control loops, local accessibility becomes restricted, constraining conventional local parity control and detection. While Andreev molecules offer a platform for non-local intervention, deterministic protocols for parity manipulation have yet to be experimentally established. Here, we demonstrate deterministic non-local control over the parity configuration of a quantum dot (QD) by electrically modulating the coherent hybridization with a spatially adjacent QD within an Andreev molecule. By systematically investigating three distinct joint parity configuration regimes in the elastic co-tunneling limit, we experimentally uncover the operational conditions for this non-local control. In conjunction with theoretical simulations establishing a global phase diagram, we identify a set of universal selection rules governing parity transitions, dictated by the symmetry-imposed interplay between the joint parity configuration and the dominant inter-dot coupling mechanism (elastic co-tunneling vs. crossed Andreev reflection). Furthermore, we establish the supercurrent, directly signaled by zero-bias conductance peaks, as an intrinsic, sensor-free probe of the parity configuration, obviating the need for auxiliary charge sensors. Our results provide a validated physical framework for parity engineering, offering a key building block for scalable, multi-QD superconducting architectures.

cond-mat.mes-hall↗

arXiv2025

Ubiquitous missing first Shapiro step in Al-InSb nanosheet Josephson junctions

The absence of odd-order Shapiro steps is a predicted signature of topological superconductors. Experimentally, the missing first-order Shapiro step has been reported in both putative topological superconducting systems and topologically trivial superconductor-semiconductor Josephson junctions. Here, we revisit this phenomenon in topologically trivial Al-InSb nanosheet Josephson junctions under microwave irradiation. The missing first Shapiro step coincides with a sharp voltage jump during superconducting switching, yet reappears when the jump is lowered and softened by increasing microwave power, temperature, or magnetic field. It also reappears at higher microwave frequencies, consistent with qualitative results from an RSJ model incorporating the sharp jump. These observations indicate that the absence of the first Shapiro step, associated with the sharp switching jump, simply results from their location within the measurement blind region. This work identifies a common but overlooked mechanism underlying the missing first Shapiro step, offering new insights into fractional Josephson effect experiments.

cond-mat.supr-con↗

arXiv2025

Highly robust factored principal component analysis for matrix-valued outlier accommodation and explainable detection via matrix minimum covariance determinant

Principal component analysis (PCA) is a classical and widely used method for dimensionality reduction, with applications in data compression, computer vision, pattern recognition, and signal processing. However, PCA is designed for vector-valued data and encounters two major challenges when applied to matrix-valued data with heavy-tailed distributions or outliers: (1) vectorization disrupts the intrinsic matrix structure, leading to information loss and the curse of dimensionality, and (2) PCA is highly sensitive to outliers. Factored PCA (FPCA) addresses the first issue through probabilistic modeling, using a matrix normal distribution that explicitly represents row and column covariances via a separable covariance structure, thereby preserving the two-way dependency and matrix form of the data. Building on FPCA, we propose highly robust FPCA (HRFPCA), a robust extension that replaces maximum likelihood estimators with the matrix minimum covariance determinant (MMCD) estimators. This modification enables HRFPCA to retain FPCA's ability to model matrix-valued data while achieving a breakdown point close to 50\%, substantially improving resistance to outliers. Furthermore, HRFPCA produces the score--orthogonal distance analysis (SODA) plot, which effectively visualizes and classifies matrix-valued outliers. Extensive simulations and real-data analyses demonstrate that HRFPCA consistently outperforms competing methods in robustness and outlier detection, underscoring its effectiveness and broad applicability.

stat.ME↗

arXiv2025

Josephson diode effect in nanowire-based Andreev molecules

Superconducting systems exhibit non-reciprocal current transport under certain conditions of symmetry breaking, a phenomenon known as the superconducting diode effect. This effect allows for perfect rectification of supercurrent, and has received considerable research interest. We report the observation of the Josephson diode effect (JDE) in nanowire-based Andreev molecules, where the time-reversal and spatial-inversion symmetries of a Josephson junction (JJ) can be nonlocally broken by coherently coupling to another JJ. The JDE can be controlled using both non-local phase and gate voltages. Notably, the non-local phase can induce a sign reversal of the diode efficiency, a manifestation of regulating the probabilities of double elastic cotunneling and double-crossed Andreev reflection. Additionally, the diode efficiency can be further modulated by local and non-local gate voltages, exhibiting a central-peak feature in the gate-voltage space. Our theoretical calculations of the energy spectrum and the Josephson currents align well with the experimental results. These results demonstrate the non-local regulation of the JDE in Andreev molecules, offering significant implications for the control of multi-JJ devices and the development of advanced superconducting devices.

cond-mat.mes-hall↗

arXiv2025

Density of States (Gate) - Controlled Andreev Molecule and Sensor

Topological quantum computing typically relies on topological Andreev bound states (ABSs) engineered in hybrid superconductor-semiconductor devices, where gate control offers key advantages. While strong Zeeman fields can induce such states, an alternative approach emerges through Andreev molecules -- closely spaced, coupled ABSs, also key building-block for Kitaev chain -- that enable topological behavior without high magnetic fields. However, existing Andreev molecules are controlled via magnetic flux in superconducting loops, limiting scalability. Here, we introduce a gate-controlled Andreev molecule, where electrostatic tuning of the density of states in one site nonlocally enhances the critical current of another. This eliminates superconducting loops, offering superior tunability, scalability, and sensitivity. We further extend such an Andreev molecule to a multi-site Kitaev chain, and a noninvasive sensor resolving single-Cooper-pair charge for parity readout. This platform bridges the gap between scalable ABS engineering and high-sensitivity quantum sensing, advancing the development for constructing and parity-readout in topological ABSs and long Kitaev chains towards topological qubits.

cond-mat.mes-hall↗

arXiv2025

Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference

Heterogeneous Graph Neural Networks (HGNNs) have achieved promising results in various heterogeneous graph learning tasks, owing to their superiority in capturing the intricate relationships and diverse relational semantics inherent in heterogeneous graph structures. However, the neighborhood-fetching latency incurred by structure dependency in HGNNs makes it challenging to deploy for latency-constrained applications that require fast inference. Inspired by recent GNN-to-MLP knowledge distillation frameworks, we introduce HG2M and HG2M+ to combine both HGNN's superior performance and MLP's efficient inference. HG2M directly trains student MLPs with node features as input and soft labels from teacher HGNNs as targets, and HG2M+ further distills reliable and heterogeneous semantic knowledge into student MLPs through reliable node distillation and reliable meta-path distillation. Experiments conducted on six heterogeneous graph datasets show that despite lacking structural dependencies, HG2Ms can still achieve competitive or even better performance than HGNNs and significantly outperform vanilla MLPs. Moreover, HG2Ms demonstrate a 379.24$\times$ speedup in inference over HGNNs on the large-scale IGB-3M-19 dataset, showcasing their ability for latency-sensitive deployments.

cs.LG↗

arXiv2025

Circuit-level-configurable Zero-field Superconducting Diodes: A Universal Platform Beyond Intrinsic Symmetry Breaking

Modern industry seeks next-generation microelectronics with ultra-low dissipation and noise beyond semiconducting systems, where the superconducting electronics offer promise. Its physical foundation is the superconducting diode effect (SDE) with nonreciprocal supercurrent. SDE has hitherto mainly relied on material-specific intrinsic symmetry breaking in superconductors, suffering from low yield, controllability, and compatibility with further functional extension - an undesirable aspect for applications. Here, we demonstrated a field-free SDE due to the chemical potential shift from external circuit line resistance, which is generic and challenges the previous interpretations of the intrinsic symmetry breaking in superconductivity for zero-field SDE. Moreover, this SDE is circuit-level configurable since it can be electrically switched on/off with its polarity and efficiency precisely modulated via gate voltage and circuit reconfiguration, facilitating functional extension. Such a generic, controllable and extensible SDE addresses critical challenges in dissipationless circuit towards application, and thus establishes a robust platform for scalable superconducting electronics.

cond-mat.supr-con↗

arXiv2025

Gate modulation and interface engineering on Coulomb blockade in open superconducting islands

Mesoscopic Coulomb blockade (MCB) is recognized as a phase-coherent variant of the conventional Coulomb blockade that arises in systems with open contacts. In open quantum dots, MCB is enhanced by a decrease in background conductance. This occurs because the reduction in coupling strength between the quantum dot and the outer reservoir renders the system more closed, thereby facilitating the emergence of conventional Coulomb blockade. In this work, we demonstrate that the MCB in open superconducting islands exhibits an different correlation with coupling strength compared to open quantum dots. Specifically, a decrease in background conductance may result in a weakening of the MCB. This observation indicates that the MCB in superconducting islands originates from the presence of superconducting-normal interfaces.

cond-mat.mes-hall↗

arXiv2025

Matrix Healy Plot: A Practical Tool for Visual Assessment of Matrix-Variate Normality

Matrix-valued data, where each observation is represented as a matrix, frequently arises in various scientific disciplines. Modeling such data often relies on matrix-variate normal distributions, making matrix-variate normality testing crucial for valid statistical inference. Recently, the Distance-Distance (DD) plot has been introduced as a graphical tool for visually assessing matrix-variate normality. However, the Mahalanobis squared distances (MSD) used in the DD plot require vectorizing matrix observations, restricting its applicability to cases where the dimension of the vectorized data does not exceed the sample size. To address this limitation, we propose a novel graphical method called the Matrix Healy (MHealy) plot, an extension of the Healy plot for vector-valued data. This new plot is based on more accurate matrix-based MSD that leverages the inherent structure of matrix data. Consequently, it offers a more reliable visual assessment. Importantly, the MHealy plot eliminates the sample size restriction of the DD plot and hence more applicable to matrix-valued data. Empirical results demonstrate its effectiveness and practicality compared to the DD plot across various scenarios, particularly in cases where the DD plot is not available due to limited sample sizes.

stat.ME↗

arXiv2025

Gate Tunable Josephson Diode Effect in Josephson Junctions made from InAs Nanosheets

We report the observation of Josephson diode effect (JDE) in hybrid devices made from semiconductor InAs nanosheets and superconductor Al contacts. By applying an in-plane magnetic field ($B_{\mathrm{xy}}$), we detect non-reciprocal superconducting switching current as well as non-reciprocal superconducting retrapping current. The strength of the JDE depends on the angle between the in-plane magnetic field and the bias current ($I_{\mathrm{b}}$), reaching its maximum when $B_{\mathrm{xy}} \perp I_{\mathrm{b}}$ and dropping to nearly zero when $B_{\mathrm{xy}}\parallel I_{\mathrm{b}}$. Additionally, the diode efficiency is tunable via an electrostatic gate with a complete suppression at certain gate voltages. Our findings indicate that the observed JDE in InAs nanosheet-based Josephson junctions most likely arises from the Rashba spin-orbit interaction (SOI) in the nanosheets. Such gate-tunable JDE in Josephson junctions made from semiconductor materials with SOI is useful not only for constructing advanced superconducting electronics but also for detecting novel superconducting states.

cond-mat.mes-hall↗

arXiv2025

Tunable superconducting diode effect in higher-harmonic InSb nanosheet interferometers

Superconducting diodes, characterized by the nonreciprocal supercurrent flow, have gained significant attention for their potential in dissipationless electronics. This study presents a superconducting quantum interference device (SQUID) composed of two Al-InSb nanosheet Josephson junctions. Utilizing prepatterned local backgates, we achieve a gate- and flux-tunable superconducting diode with controllable efficiency in both amplitude and sign. Numerical simulations attribute the diode effect to higher harmonics in the current-phase relation. Crucially, fractional Shapiro step experiments provide direct insights into the evolution of these higher harmonics with flux tuning, showcasing significant enhancements in the second-harmonic signatures of the SQUID near half-integer flux quanta. Furthermore, we investigate the microwave-assisted diode response and experimentally show that the polarity of the diode effect can be switched by the microwave power. These results demonstrate the potential of InSb nanosheet-based hybrid devices as highly tunable elements for use in dissipationless electronics.

cond-mat.supr-con↗

arXiv2025

Learning Accurate, Efficient, and Interpretable MLPs on Multiplex Graphs via Node-wise Multi-View Ensemble Distillation

Multiplex graphs, with multiple edge types (graph views) among common nodes, provide richer structural semantics and better modeling capabilities. Multiplex Graph Neural Networks (MGNNs), typically comprising view-specific GNNs and a multi-view integration layer, have achieved advanced performance in various downstream tasks. However, their reliance on neighborhood aggregation poses challenges for deployment in latency-sensitive applications. Motivated by recent GNN-to-MLP knowledge distillation frameworks, we propose Multiplex Graph-Free Neural Networks (MGFNN and MGFNN+) to combine MGNNs' superior performance and MLPs' efficient inference via knowledge distillation. MGFNN directly trains student MLPs with node features as input and soft labels from teacher MGNNs as targets. MGFNN+ further employs a low-rank approximation-based reparameterization to learn node-wise coefficients, enabling adaptive knowledge ensemble from each view-specific GNN. This node-wise multi-view ensemble distillation strategy allows student MLPs to learn more informative multiplex semantic knowledge for different nodes. Experiments show that MGFNNs achieve average accuracy improvements of about 10% over vanilla MLPs and perform comparably or even better to teacher MGNNs (accurate); MGFNNs achieve a 35.40$\times$-89.14$\times$ speedup in inference over MGNNs (efficient); MGFNN+ adaptively assigns different coefficients for multi-view ensemble distillation regarding different nodes (interpretable).

cs.LG↗

arXiv2024

HardRace: A Dynamic Data Race Monitor for Production Use

Data races are critical issues in multithreaded program, leading to unpredictable, catastrophic and difficult-to-diagnose problems. Despite the extensive in-house testing, data races often escape to deployed software and manifest in production runs. Existing approaches suffer from either prohibitively high runtime overhead or incomplete detection capability. In this paper, we introduce HardRace, a data race monitor to detect races on-the-fly while with sufficiently low runtime overhead and high detection capability. HardRace firstly employs sound static analysis to determine a minimal set of essential memory accesses relevant to data races. It then leverages hardware trace instruction, i.e., Intel PTWRITE, to selectively record only these memory accesses and thread synchronization events during execution with negligible runtime overhead. Given the tracing data, HardRace performs standard data race detection algorithms to timely report potential races occurred in production runs. The experimental evaluations show that HardRace outperforms state-of-the-art tools like ProRace and Kard in terms of both runtime overhead and detection capability -- HardRace can detect all kinds of data races in read-world applications while maintaining a negligible overhead, less than 2% on average.

cs.SE↗