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Researcher profile

Qian Tao

Qian Tao contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer Visioncond-mat.supr-coneess.IVMachine Learningcond-mat.str-elcond-mat.mtrl-sciHuman-Computer Interactionphysics.flu-dyn

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Published work

11 published item(s)

preprint2026arXiv

Field-induced magnetic phase transitions and transport anomalies in GdAlSi

Magnetic topological materials hosting non-zero Berry curvature have emerged as a focus of intensive research due to their exceptional magnetoelectric coupling phenomena and potential applications in next-generation spintronic devices. In this work, we successfully synthesized high-quality GdAlSi single crystals, a prototypical member of RAlX (R = rare earth elements; X = Si/Ge) family that has been theoretically predicted to sustain a non-trivial Weyl semimetal state. Through systematic investigations of magnetic and transport properties, we identified two successive antiferromagnetic transitions at critical temperatures TN1 31.9 K and TN2 31.1 K, as evidenced by temperature-dependent resistivity, magnetic susceptibility, and specific heat measurements. Notably, applied magnetic fields exceeding 8 T induce a third magnetic transition (TN3), generating a cascade of metamagnetic transitions that collectively form a dendritic phase diagram. This complex magnetic behavior is attributed to the interplay between localized Gd-4f moments and itinerant conduction electrons, possibly mediated by Dzyaloshinskii-Moriya interactions. Transport measurements revealed striking stepwise anomalies in magnetoresistance when crossing phase boundaries, accompanied by pronounced hysteresis loops arising from magnetic moment flopping processes. Our results not only establish GdAlSi as a rich platform for investigating correlated topological states, but also demonstrate its potential for engineering topological phase transitions through magnetic symmetry manipulation in Weyl semimetals.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges

Medical image registration is critical for clinical applications, and fair benchmarking of different methods is essential for monitoring ongoing progress in the field. To date, the Learn2Reg 2020-2023 challenges have released several complementary datasets and established metrics for evaluations. Building on this foundation, the 2024 edition expands the challenge's scope to cover a wider range of registration scenarios, particularly in terms of modality diversity and task complexity, by introducing three new tasks, including large-scale multi-modal registration and unsupervised inter-subject brain registration, as well as the first microscopy-focused benchmark within Learn2Reg. The new datasets also inspired new method developments, including invertibility constraints, pyramid features, keypoints alignment and instance optimisation. Visit Learn2Reg at https://learn2reg.grand-challenge.org.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Set-Based Groupwise Registration for Variable-Length, Variable-Contrast Cardiac MRI

Quantitative cardiac magnetic resonance imaging (MRI) enables non-invasive myocardial tissue characterization but relies on robust motion correction within these variable-length, variable-contrast image sequences. Groupwise registration, which simultaneously aligns all images, has shown greater robustness than pairwise registration for motion correction. However, current deep-learning-based groupwise registration methods cannot generalize across MRI sequences: the architecture typically encodes input data as a fixed-length channel stack, which rigidly couples network design to protocol-specific sequence length, input ordering, and contrast dynamics. At inference time, any change in imaging protocols will render the network unusable. In this work, we introduce \emph{\AnyTwoReg}, a new set-based groupwise registration framework that takes a quantitative MRI sequence as an unordered set. This set formulation fundamentally decouples network design from sequence length and input ordering. By utilizing a shared encoder and correlation-guided feature aggregation, \emph{\AnyTwoReg} constructs a permutation-invariant canonical reference for registration, and learns a permutation-equivariant mapping from images to deformation fields. Additionally, we extract contrast-insensitive image features from an existing foundation model to handle extreme contrast variations. Trained exclusively on a single public $T_1$ mapping dataset (STONE, sequence length $L=11$), \AnyTwoReg generalizes to two unseen quantitative MRI datasets (MOLLI, ASL) with variable lengths ($L \in [11, 60]$) and different contrast dynamics. It achieves strong cross-protocol generalization in a zero-shot manner, and consistently improves downstream quantitative mapping quality. Notably, while designed for quantitative MRI sequences, our framework is directly applicable to Cine MRI sequences for inter-cardiac-phase registration.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Giant Nernst effect in the crossover between Fermi liquid and strange metal

The strange-metal state is a crucial problem in condensed matter physics highlighted by its ubiquity in almost all major correlated systems[1-7]. Its understanding could provide important insight into high-Tc superconductivity[2] and quantum criticality[8]. However, with the Fermi liquid theory failing in strange metals, understanding the highly unconventional behaviors has been a long-standing challenge. Fundamental aspects of strange metals remain elusive, including the nature of their charge carriers[1]. Here, we report the observation of a giant Nernst response in the strange-metal state in a two-dimensional superconductor 2M-WS2. A giant Nernst coefficient comparable to the vortex Nernst signal in superconducting cuprates, and its high sensitivity to carrier mobility, are found when the system enters the strange-metal state from the Fermi liquid state. The temperature and magnetic field dependence of the giant Nernst peak rule out the relevance of both Landau quasiparticles and superconductivity. Instead, the giant Nernst peak at the crossover indicates a dramatic change in carrier entropy when entering the strange-metal state. The presence of such an anomalous Nernst response is further confirmed in other iconic strange metals, suggesting its universality and places stringent experimental constraints on the mechanism of strange metals.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Deep Recursive Embedding for High-Dimensional Data

t-distributed stochastic neighbor embedding (t-SNE) is a well-established visualization method for complex high-dimensional data. However, the original t-SNE method is nonparametric, stochastic, and often cannot well prevserve the global structure of data as it emphasizes local neighborhood. With t-SNE as a reference, we propose to combine the deep neural network (DNN) with the mathematical-grounded embedding rules for high-dimensional data embedding. We first introduce a deep embedding network (DEN) framework, which can learn a parametric mapping from high-dimensional space to low-dimensional embedding. DEN has a flexible architecture that can accommodate different input data (vector, image, or tensor) and loss functions. To improve the embedding performance, a recursive training strategy is proposed to make use of the latent representations extracted by DEN. Finally, we propose a two-stage loss function combining the advantages of two popular embedding methods, namely, t-SNE and uniform manifold approximation and projection (UMAP), for optimal visualization effect. We name the proposed method Deep Recursive Embedding (DRE), which optimizes DEN with a recursive training strategy and two-stage losse. Our experiments demonstrated the excellent performance of the proposed DRE method on high-dimensional data embedding, across a variety of public databases. Remarkably, our comparative results suggested that our proposed DRE could lead to improved global structure preservation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Deep Recursive Embedding for High-Dimensional Data

Embedding high-dimensional data onto a low-dimensional manifold is of both theoretical and practical value. In this paper, we propose to combine deep neural networks (DNN) with mathematics-guided embedding rules for high-dimensional data embedding. We introduce a generic deep embedding network (DEN) framework, which is able to learn a parametric mapping from high-dimensional space to low-dimensional space, guided by well-established objectives such as Kullback-Leibler (KL) divergence minimization. We further propose a recursive strategy, called deep recursive embedding (DRE), to make use of the latent data representations for boosted embedding performance. We exemplify the flexibility of DRE by different architectures and loss functions, and benchmarked our method against the two most popular embedding methods, namely, t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP). The proposed DRE method can map out-of-sample data and scale to extremely large datasets. Experiments on a range of public datasets demonstrated improved embedding performance in terms of local and global structure preservation, compared with other state-of-the-art embedding methods.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Density-Aware Hyper-Graph Neural Networks for Graph-based Semi-supervised Node Classification

Graph-based semi-supervised learning, which can exploit the connectivity relationship between labeled and unlabeled data, has been shown to outperform the state-of-the-art in many artificial intelligence applications. One of the most challenging problems for graph-based semi-supervised node classification is how to use the implicit information among various data to improve the performance of classifying. Traditional studies on graph-based semi-supervised learning have focused on the pairwise connections among data. However, the data correlation in real applications could be beyond pairwise and more complicated. The density information has been demonstrated to be an important clue, but it is rarely explored in depth among existing graph-based semi-supervised node classification methods. To develop a flexible and effective model for graph-based semi-supervised node classification, we propose a novel Density-Aware Hyper-Graph Neural Networks (DA-HGNN). In our proposed approach, hyper-graph is provided to explore the high-order semantic correlation among data, and a density-aware hyper-graph attention network is presented to explore the high-order connection relationship. Extensive experiments are conducted in various benchmark datasets, and the results demonstrate the effectiveness of the proposed approach.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Two-dimensional superconductivity at the surfaces of KTaO3 gated with ionic liquid

The recent observation of superconductivity at the interfaces between KTaO3 and EuO (or LaAlO3) offers a new example of emergent phenomena at oxide interfaces. This superconductivity exhibits an unusual strong dependence on the crystalline orientation of KTaO3 and its superconducting transition temperature Tc is nearly one order of magnitude higher than that of the seminal LaAlO3/SrTiO3 interface. To understand its mechanism, it is crucial to address if the formation of oxide interfaces is indispensable for the presence of superconductivity. Here, by exploiting ionic liquid (IL) gating, we obtain superconductivity at KTaO3 (111) and (110) surfaces with Tc up to 2.0 K and 1.0 K, respectively. This oxide-interface-free superconductivity gives a clear experimental evidence that the essential physics of KTaO3 interface superconductivity lies in the KTaO3 surfaces doped with electrons. Moreover, the ability to control superconductivity at surfaces with IL provides a simple way to study the intrinsic superconductivity in KTaO3.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation of LGE-MRIs is challenging due to its attenuated contrast. Since most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atrium segmented by three medical experts, ultimately attracting the participation of 27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis, offering an overall picture of the major design choices of convolutional neural networks (CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm, significantly outperforming prior state-of-the-art. Particularly, our analysis demonstrated that double, sequentially used CNNs, in which a first CNN is used for automatic region-of-interest localization and a subsequent CNN is used for refined regional segmentation, achieved far superior results than traditional methods and pipelines containing single CNNs. This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Frozen Patterns of Impacted Droplets: From Conical Tips to Toroidal Shapes

We report frozen patterns for the water droplets impacting on a cold substrate through fast-speed images. These patterns can be manipulated by several physical parameters (the droplet size, falling height, and substrate temperature), and the scaling analysis has a remarkable agreement with the phase diagram. The observed double-concentric toroidal shape is attributed to the correlation between the impacting dynamics and freezing process, as confirmed by the spatiotemporal evolution of the droplet temperature, the identified timescale associated with the morphology and solidification ($t_{inn}\simeq τ_{sol}$), and the ice front-advection model. These results for frozen patterns provide insight into the complex interplay of the rapid impacting hydrodynamics, the transient heat transfer, and the intricate solidification process.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Type-I superconductivity in noncentrosymmetric NbGe$_{2}$

Single crystals of NbGe$_{2}$ which crystallize in a noncentrosymmetric hexagonal structure with chirality are synthesized and their superconductivity is investigated. Type-I superconductivity is confirmed by dc magnetization, field-induced second-to first-order phase transition in specific heat, and a small Ginzburg-Landau parameter $κ_{GL}=0.12$. The isothermal magnetization measurements show that there is a crossover from type-I to type-II/1 superconductivity with decreasing temperature and an unusually enhanced surface superconducting critical field ($H_{c3}$) is discovered. The band structure calculations indicate the presence of Kramer-Weyl nodes near the Fermi level. These observations demonstrate that NbGe$_{2}$ is an interesting and rare example involving the possible interplay of type-I superconductivity, noncentrosymmetric structure and topological properties.

0 citations0 reviewsNo code linkedOpen access