arXiv subjects

Liang Qiao

Publications and source records attributed to Liang Qiao.

At least 19 recordsLinked to original sources

arXiv2026

Evidence for Clean d-wave Superconductivity in Samarium Nickelates

The discovery of superconducting nickelates provides a unique opportunity to explore the pairing mechanism of high-temperature superconductivity. Here, we use ultrafast terahertz spectroscopy to probe the temperature-dependent superfluid density in an infinite-layer samarium nickelate film with a Tc of 20 K. The superfluid density decreases linearly with rising temperature, consistent with clean limit d-wave pairing. From this linear relation, we extract a superconducting gap of 2.5 meV and a gap-to-Tc ratio of 3, suggesting that this sample lies in the weak-coupling limit. Furthermore, the ratio of the mean free path to the coherence length, is determined to be 1.5, confirming the clean-limit behavior. These findings establish strong parallels between the pairing mechanisms in nickelate and cuprate superconductors.

cond-mat.supr-con↗

arXiv2026

Emergent quantum phenomena via phase-coherence engineering in infinite-layer nickelate superconductors

Dimensionality of a physical system, conventionally an invariant geometric characteristic, fundamentally governs the universality class of phase transitions and the landscape of emergent collective phenomena. In low-dimensional or layered high-temperature superconductors, the macroscopic phase coherence of superconducting orders is typically confined in two dimensions, underscoring the critical role of phase fluctuations in determining the overall phase diagrams. Here, we strategically enhance the phase fluctuations by fabricating periodically arranged nano-holes in the infinite-layer nickelate superconducting films, effectively constructing Josephson junction arrays. In the nano-patterned films, the weakening of macroscopic phase coherence drives a two-stage superconducting transition towards an anomalous metallic ground state with saturated resistance. The emergence of charge-2e quantum oscillations manifests the coherence across the array, while an anomalous zero-field magnetoresistance peak signifies the extreme quantum phase fluctuations persisting to ultralow temperatures. Remarkably, with quantum fluctuations enhanced synergistically by nano-patterning and magnetic fields, an anomalous reversal of superconducting anisotropy is observed in Nd-nickelates, where in-plane critical fields fall below out-of-plane values. The evolution of anisotropy may unmask an internal exchange-Zeeman field coupled to the collective electronic states. Our results unveil how superconductivity evolves in response to phase fluctuations, establishing nano-patterning as a powerful paradigm to uncover hidden intertwined orders in strongly correlated systems.

cond-mat.supr-con↗

arXiv2025

Facet Specific Electron Conduction in Pentavalent (W5+) WO3 Drives Superior Photocatalytic CO 2 Reduction in (002) Plane

This article reports a concept of heat-induced topological modifications of non-layered WO 3 followed by successful synthesis of oxygen-vacant more-porous nanosheets with exposed active (002) facet. Experimental measurements and Density Functional Theory (DFT) calculations have revealed that the photoexcited electrons are found to accumulate preferentially on (002) facet to yield enhanced electron conduction, and consequently, strengthen the reduction potential as active catalytic sites for photocatalytic CO2 reduction. Owing to these beneficial properties, the more-porous nanosheets of WO 3 with (002) facet have exhibited superior performance than that of less-porous nanosheets of WO3 with (220) facet and bulk WO3 with (205) facet. This study therefore provides a new understanding of regulating physical, optical, and electronic properties through intricate atomic structure modulation of WO3, and may find widespread application in optoelectronics, sensors, and energy conversion.

cond-mat.mtrl-sci↗

arXiv2025

FlashOmni: A Unified Sparse Attention Engine for Diffusion Transformers

Multi-Modal Diffusion Transformers (DiTs) demonstrate exceptional capabilities in visual synthesis, yet their deployment remains constrained by substantial computational demands. To alleviate this bottleneck, many sparsity-based acceleration methods have been proposed. However, their diverse sparsity patterns often require customized kernels for high-performance inference, limiting universality. We propose FlashOmni, a unified sparse attention engine compatible with arbitrary DiT architectures. FlashOmni introduces flexible sparse symbols to standardize the representation of a wide range of sparsity strategies, such as feature caching and block-sparse skipping. This unified abstraction enables the execution of diverse sparse computations within a single attention kernel. In addition, FlashOmni designs optimized sparse GEMMs for attention blocks, leveraging sparse symbols to eliminate redundant computations and further improve efficiency. Experiments demonstrate that FlashOmni delivers near-linear, closely matching the sparsity ratio speedup (1:1) in attention and GEMM-$Q$, and achieves 2.5$\times$-3.8$\times$ acceleration in GEMM-$O$ (max peaking at about 87.5% of the theoretical limit). Applied with a multi-granularity sparsity strategy, it enables the Hunyuan model (33K) to achieve about 1.5$\times$ end-to-end acceleration without degrading visual quality.

cs.LG↗

arXiv2025

Thermoelectricity evidence for quantum criticality in clean infinite-layer nickelate films

We investigate the Seebeck coefficient ($S$) in infinite-layer nickelate films with different disorder levels. The disordered NdNiO$_{2}$ film exhibits a flat $S/T$ curve, whereas cleaner samples display a logarithmic divergence with decreasing temperature, followed by a pronounced ``hump'' near 25 K. These distinct behaviors reveal a disorder-driven transition from band-structure-dominated transport to quantum-critical-dominated transport. Below the ``hump'' temperature, four-fold symmetry breaking is observed in the in-plane angular magnetoresistance, indicating the presence of short-range antiferromagnetic order in parent infinite-layer nickelate films. Furthermore, the logarithmic divergence in $S/T$ is also observed in a clean superconducting Sm$_{0.73}$Ca$_{0.05}$Eu$_{0.22}$NiO$_{2}$ film, where it coexists with linear-in-temperature resistivity over the same temperature range. These findings demonstrate the existence of quantum criticality over a wide doping range in clean infinite-layer nickelate films, similar to cuprates, which highlights the central role of antiferromagnetic spin correlations in their superconducting pairing mechanisms.

cond-mat.supr-con↗

arXiv2025

Pruner: A Draft-then-Verify Exploration Mechanism to Accelerate Tensor Program Tuning

Tensor program tuning is essential for the efficient deployment of deep neural networks. Search-based approaches have demonstrated scalability and effectiveness in automatically finding high-performance programs for specific hardware. However, the search process is often inefficient, taking hours or even days to discover optimal programs due to the exploration mechanisms guided by an accurate but slow-learned cost model. Meanwhile, the learned cost model trained on one platform cannot seamlessly adapt online to another, which we call cross-platform online unawareness. In this work, we propose Pruner and MoA-Pruner. Pruner is a "Draft-then-Verify" exploration mechanism that accelerates the schedule search process. Instead of applying the complex learned cost model to all explored candidates, Pruner drafts small-scale potential candidates by introducing a naive Symbol-based Analyzer (draft model), then identifies the best candidates by the learned cost model. MoA-Pruner introduces a Momentum online Adaptation strategy to address the cross-platform online unawareness. We incorporate Pruner into the TVM and conduct extensive experiments on three GPU-based platforms. Results show considerable speedup in schedule search time. In online tuning scenarios, Pruner and MoA-Pruner achieve an average speedup of $2.6 \times$ and $4.82 \times$ compared to Ansor. In offline tuning scenarios, Pruner achieves an average speedup of $4.75 \times$ and $4.05\times$ compared to TenSet and TLP, respectively. Furthermore, Pruner achieves an average speedup of $4.08 \times$ compared to MetaSchedule on TensorCore.

cs.LG↗

arXiv2025

Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis; improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and ultimately enabling AI-empowered virtual cells.

q-bio.OT↗

arXiv2025

Superconductivity Favored Anisotropic Phase Stiffness in Infinite-Layer Nickelates

In unconventional superconductors such as cuprates and iron pnictides and chalcogenides, phase stiffness - a measure of the energy cost associated with superconducting phase variations - is on the same order of magnitude as the strength of Cooper pairing, translating to superconductivity governed by phase fluctuations. However, due to a lack of a direct experimental probe, there remains a fundamental gap in establishing microscopic picture between unconventional superconductivity and phase fluctuations. Here we show a vector current technique that allows for in-situ angle-resolved transport measurements, providing exclusive evidence suggesting an anisotropic nature of phase stiffness in infinite-layer nickelate superconductors. Pronounced anisotropy of in-plane resistance manifests itself in both normal and superconducting transition states, indicating crystal symmetry breaking. Remarkably, the electric conductivity of Nd0.8Sr0.2NiO2 peaks at 125° between the direction of the current and crystal principal axis, but this angle evolves to 160° near zero-resistance temperature. Further measurements reveal that the superconductivity is favored along a direction with minimized phase fluctuations, an orientation strikingly deviating from the symmetric direction imposed by both electronic anisotropy and the underlying crystal lattice. Identical measurements conducted on a prototypical cuprate superconductor yield consistent results, suggesting that this previously unknown behavior could be ubiquitous. By shielding insight into the contrasting anisotropy between electron fluid and superfluid, our findings provide clues for a unified framework for understanding unconventional superconductors

cond-mat.supr-con↗

arXiv2024

A Strong Baseline for Semi-Supervised Incremental Few-Shot Learning

Few-shot learning (FSL) aims to learn models that generalize to novel classes with limited training samples. Recent works advance FSL towards a scenario where unlabeled examples are also available and propose semi-supervised FSL methods. Another line of methods also cares about the performance of base classes in addition to the novel ones and thus establishes the incremental FSL scenario. In this paper, we generalize the above two under a more realistic yet complex setting, named by Semi-Supervised Incremental Few-Shot Learning (S2 I-FSL). To tackle the task, we propose a novel paradigm containing two parts: (1) a well-designed meta-training algorithm for mitigating ambiguity between base and novel classes caused by unreliable pseudo labels and (2) a model adaptation mechanism to learn discriminative features for novel classes while preserving base knowledge using few labeled and all the unlabeled data. Extensive experiments on standard FSL, semi-supervised FSL, incremental FSL, and the firstly built S2 I-FSL benchmarks demonstrate the effectiveness of our proposed method.

cs.CV↗

arXiv2024

In-depth Understanding of the Band Alignment and Interface States Scenario in Bi$_2$O$_2$Se/SrTiO$_3$ Ultrathin Heterojunction

Bismuth oxyselenide (Bi$_2$O$_2$Se), a novel quasi-2D charge-carrying semiconductor, is hailed as one of the best emerging platforms for the next generation semiconductor devices. Recent efforts on developing diverse Bi$_2$O$_2$Se heterojunctions have produced extensive potential applications in electronics and optoelectronics. In-depth understanding of the band alignment and especially interface dynamics is, however, still challenging. In this work, a comprehensive experimental investigation on the band alignment is performed by a high-resolution X-ray photoelectron spectrometer (HRXPS), and the properties of interface states are also fully discussed. The results show that the ultrathin film Bi$_2$O$_2$Se grown on SrTiO$_3$ (TiO$_2$ (001) termination) exhibits Type-I (straddling gap) band alignment with a valence band offset (VBO) of about 1.77\pm0.04 eV and conduction band offset (CBO) of about 0.68\pm0.04 eV. However, further considering the contribution of the interface states, the bands on the interface present a herringbone configuration due to sizable build-in electric fields, which is significantly different from the conventional band alignment. In this sense, our results provide an insightful guidance to the development of high-efficiency electronic and optoelectronic devices, specifically of the devices where the charge transfer is highly sensitive to interface states.

cond-mat.mtrl-sci↗

arXiv2024

Controllable and Fast Growth of High-Quality Atomically Thin and Atomically Flat Bi$_2$O$_2$Se Films

As a novel and promising 2D material, bismuth oxyselenide (Bi$_2$O$_2$Se) has demonstrated significant potential to overcome existing technical barriers in various electronic device applications, due to its unique physical properties like high symmetry, adjustable electronic structure, ultra-high electron mobility. However, the rapid growth of Bi$_2$O$_2$Se films down to a few atomic layers with precise control remains a significant challenge. In this work, the growth of two-dimensional (2D) Bi$_2$O$_2$Se thin films by the pulsed laser deposition (PLD) method is systematically investigated. By controlling temperature, oxygen pressure, laser energy density and laser emission frequency, we successfully prepare atomically thin and flat Bi$_2$O$_2$Se (001) thin films on the (001) surface of SrTiO3. Importantly, we provide a fundamental and unique perspective toward understanding the growth process of atomically thin and flat Bi$_2$O$_2$Se films, and the growth process can be primarily summarized into four steps: i) anisotropic non-spontaneous nucleation preferentially along the step roots; ii) monolayer Bi$_2$O$_2$Se nanosheets expanding across the surrounding area, and eventually covering the entire STO substrate step; iii) vertical growth of Bi$_2$O$_2$Se monolayer in a 2D Frank-van der Merwe (FM) epitaxial growth, and iv) with a layer-by-layer 2D FM growth mode, ultimately producing an atomically flat and epitaxially aligned thin film. Moreover, the combined results of the crystallinity quality, surface morphology and the chemical states manifest the successful PLD-growth of high-quality Bi$_2$O$_2$Se films in a controllable and fast mode.

cond-mat.mtrl-sci↗

arXiv2024

Bridging Cross-task Protocol Inconsistency for Distillation in Dense Object Detection

Knowledge distillation (KD) has shown potential for learning compact models in dense object detection. However, the commonly used softmax-based distillation ignores the absolute classification scores for individual categories. Thus, the optimum of the distillation loss does not necessarily lead to the optimal student classification scores for dense object detectors. This cross-task protocol inconsistency is critical, especially for dense object detectors, since the foreground categories are extremely imbalanced. To address the issue of protocol differences between distillation and classification, we propose a novel distillation method with cross-task consistent protocols, tailored for the dense object detection. For classification distillation, we address the cross-task protocol inconsistency problem by formulating the classification logit maps in both teacher and student models as multiple binary-classification maps and applying a binary-classification distillation loss to each map. For localization distillation, we design an IoU-based Localization Distillation Loss that is free from specific network structures and can be compared with existing localization distillation losses. Our proposed method is simple but effective, and experimental results demonstrate its superiority over existing methods. Code is available at https://github.com/TinyTigerPan/BCKD.

cs.CV↗

arXiv2024

Observation of unconventional van der Waals multiferroics near room temperature

The search for two-dimensional (2D) van der Waals (vdW) multiferroics is an exciting yet challenging endeavor. Room-temperature 2D vdW few-layer multiferroic is a much bigger insurmountable obstacle. Here we report the discovery of an unconventional 2D vdW multiferroic with out-of-plane ferroelectric polarization and long-range magnetic orders in trilayer NiI2 device from 10 K to 295 K. The evolutions of magnetic domains with magnetic field, and the evolutions between ferroelectric and antiferroelectric phase have been unambiguously observed. More significantly, we realize a robust mutual control of magnetism and ferroelectricity at room temperature. The magnetic domains are manipulated by a small voltage ranging from 1 V to 6 V at 0 T and 295 K. This work opens opportunities for exploring multiferroic physics at the limit of few atomic layers.

cond-mat.mtrl-sci↗

arXiv2024

SM$^3$: Self-Supervised Multi-task Modeling with Multi-view 2D Images for Articulated Objects

Reconstructing real-world objects and estimating their movable joint structures are pivotal technologies within the field of robotics. Previous research has predominantly focused on supervised approaches, relying on extensively annotated datasets to model articulated objects within limited categories. However, this approach falls short of effectively addressing the diversity present in the real world. To tackle this issue, we propose a self-supervised interaction perception method, referred to as SM$^3$, which leverages multi-view RGB images captured before and after interaction to model articulated objects, identify the movable parts, and infer the parameters of their rotating joints. By constructing 3D geometries and textures from the captured 2D images, SM$^3$ achieves integrated optimization of movable part and joint parameters during the reconstruction process, obviating the need for annotations. Furthermore, we introduce the MMArt dataset, an extension of PartNet-Mobility, encompassing multi-view and multi-modal data of articulated objects spanning diverse categories. Evaluations demonstrate that SM$^3$ surpasses existing benchmarks across various categories and objects, while its adaptability in real-world scenarios has been thoroughly validated.

cs.CV↗

arXiv2023

Electronic properties of nickelate superconductor R3Ni2O7 with oxygen vacancies

The discovery of superconductivity in La3Ni2O7 has attracted significant research interest in the field of nickelate superconductors. Despite extensive studies on pristine La3Ni2O7, the impact of oxygen vacancies (VO), a common type of intrinsic defect in oxides, on electronic structures and superconductivity in La3Ni2O7 remains unclear. In this article, we identify the most energetically favorable location for VO formation as the oxygen atom connecting the NiO6 bilayer, resulting in a significant reduction in the lattice constant along the c-axis. Interestingly, the electronic structure undergoes notable changes, particularly for the Ni dz2 and Ni dx2-y2 orbitals. The Ni dz2 orbitals change from partially filled in the pristine La3Ni2O7 to completely filled in the presence of VO, leading to a considerable decrease of its proportion near the Fermi level. Conversely, the proportion of Ni dx2-y2 states increases due to the orbital localization and slight upward shift. Additionally, we observe a significant increase in the hopping of intra-bilayer Ni dz2 orbitals when the VO exists, but with an opposite sign, which differs greatly from the previous understanding. The inter-orbital hopping between Ni dz2 and Ni dx2-y2 orbitals also changes its sign in the presence of VO. Our results indicate that the formation of VO may be harmful to the superconductivity in La3Ni2O7, given the general assumption for the critical role of Ni dz2 in generating superconductivity. Furthermore, we suggest that Ce3Ni2O7, which shares similar electronic structures to La3Ni2O7 but has a larger lattice volume, may be a better candidate for nickelate superconductor due to its lower VO concentration.

cond-mat.mtrl-sci↗

arXiv2023

Mössbauer spectroscopy study of the magnetostructural and spin-state transitions in the breathing pyrochlore LiFeCr$_{4}$O$_{8}$

We report on investigations of the complex magnetostructural and spin-state transitions in the breathing pyrochlore LiFeCr$_{4}$O$_{8}$ by means of magnetization, Mössbauer spectroscopy, and density functional theory (DFT) calculations. Three transitions corresponding to the ferrimagnetic transition at $T_N\sim94$ K, the spin-gap transition at $T_{SG}\sim50$ K, and the magnetostructural transition at $T_{MS}\sim19$ K were observed from the $χ$(T) curve, whereas only $T_N$ and $T_{MS}$ were evidenced for the Fe site from our Mössbauer measurements, suggesting that the spin-gap transition is absent at the Fe site. This indicates that the spin-gap transition is an effect of the breathing Cr$_4$ lattice, in agreement with our DFT calculations from which we see nearly decoupled electronic states for the FeO$_4$ and CrO$_6$ units. From the temperature dependence of the hyperfine magnetic field we also observed a spin-state transition for the Fe spins at $T_{MS}$ consistent with earlier neutron diffraction measurements. These local characteristics are believed to be important for a complete understanding of the complex magnetostructural coupling effects observed in similar systems.

cond-mat.str-el↗

arXiv2023

Reply to "Comment on newly found Charge Density Waves in infinite layer Nickelates''

Charge density waves (CDW) have been reported in NdNiO$_2$ and LaNiO$_2$ thin films grown on SrTiO$_3$ substrates using Ni-$L_3$ resonant x-ray scattering in Refs. [1-3]. In their comment [arXiv:2306.15086] on these reports, Pelliciari et al. found no evidence for a CDW in a NdNiO$_2$ film by performing fixed-momentum energy-dependent measurements. Instead, they observed a nearby non-resonant scattering peak, attributed to the (101) substrate reflection, made accessible at Ni-$L_3$ due to third harmonic light contamination. Here we present fixed-momentum energy-dependent resonant inelastic x-ray scattering measurements across Ni-$L_3$ on NdNiO$_2$, used in the preceding study [1]. We see intrinsic Ni-$L_3$ energy profiles at all measured \textbf{Q} values, including a strong resonance effect at $\mathbf{Q}_\mathrm{CDW} = (-1/3, 0, 0.316)$ reciprocal lattice units. Attempts to measure the (101) substrate peak using third harmonic light at Ni-$L_3$ at I21, Diamond were unfruitful. Our results clearly demonstrate the electronic origin of the scattering peak published in Ref. [1] and lack of a detectable structural component in the peak.

cond-mat.str-el↗

arXiv2023

H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor Segmentation

Recently, deep learning methods have been widely used for tumor segmentation of multimodal medical images with promising results. However, most existing methods are limited by insufficient representational ability, specific modality number and high computational complexity. In this paper, we propose a hybrid densely connected network for tumor segmentation, named H-DenseFormer, which combines the representational power of the Convolutional Neural Network (CNN) and the Transformer structures. Specifically, H-DenseFormer integrates a Transformer-based Multi-path Parallel Embedding (MPE) module that can take an arbitrary number of modalities as input to extract the fusion features from different modalities. Then, the multimodal fusion features are delivered to different levels of the encoder to enhance multimodal learning representation. Besides, we design a lightweight Densely Connected Transformer (DCT) block to replace the standard Transformer block, thus significantly reducing computational complexity. We conduct extensive experiments on two public multimodal datasets, HECKTOR21 and PI-CAI22. The experimental results show that our proposed method outperforms the existing state-of-the-art methods while having lower computational complexity. The source code is available at https://github.com/shijun18/H-DenseFormer.

eess.IV↗