arXiv subjects

Lu Cao

Publications and source records attributed to Lu Cao.

At least 19 recordsLinked to original sources

arXiv2026

Generation of high-fluence and high-intensity hard x-ray attosecond pulses at European XFEL

By combining hard x-ray attosecond pulses from the European XFEL with total-reflection focusing x-ray optics, we generated nanofocused hard x-ray attosecond pulses with intensities and fluences comparable to the highest values attained in the hard x-ray regime. A peak intensity on the order of 10$^{20}$ W/cm$^2$ is confirmed through the observation of saturation in amplified spontaneous emission from copper atoms. These x-ray pulses enable new scientific opportunities, including the exploration of higher-order nonlinear light--matter interactions, damage-free structure determination, and coherent control of atoms and molecules.

physics.optics↗

arXiv2026

A Lightweight Transformer for Pain Recognition from Brain Activity

Pain is a multifaceted and widespread phenomenon with substantial clinical and societal burden, making reliable automated assessment a critical objective. This paper presents a lightweight transformer architecture that fuses multiple fNIRS representations through a unified tokenization mechanism, enabling joint modeling of complementary signal views without requiring modality-specific adaptations or increasing architectural complexity. The proposed token-mixing strategy preserves spatial, temporal, and time-frequency characteristics by projecting heterogeneous inputs onto a shared latent representation, using a structured segmentation scheme to control the granularity of local aggregation and global interaction. The model is evaluated on the AI4Pain dataset using stacked raw waveform and power spectral density representations of fNIRS inputs. Experimental results demonstrate competitive pain recognition performance while remaining computationally compact, making the approach suitable for real-time inference on both GPU and CPU hardware.

cs.CV↗

arXiv2026

Discovery of a hybridization-wave electronic order in a van der Waals Kondo lattice

Kondo lattice systems, in which localized magnetic moments coherently hybridize with itinerant electrons, exhibit a rich landscape of emergent quantum phenomena. Within this framework, the hybridization strength itself has been theoretically proposed as a spatially modulated order parameter, giving rise to a so-called hybridization wave. However, direct experimental evidence of this quantum state has remained an outstanding challenge. Here, we report the direct observation of a hybridization wave in the layered transition metal dichalcogenide 6R-TaS2, a naturally occurring heterostructure composed of alternating 1T- and 1H-TaS2 layers. Using scanning tunneling microscopy and spectroscopy (STM/STS), we identify the hybridization gap in 1T layer, demonstrating the establishment of a coherent Kondo lattice. Notably, we discover that the hybridization gap present a uniaxial unit-cell doubling modulation, which breaks the both translational and rotational symmetries of the underlying Star-of-David superlattice. Such unit-cell doubling is not caused by structural topography, and therefore, constitutes the real-space visualization of the hybridization-wave order. Furthermore, the hybridization wave correlates with an energy-dependent nematic order that shares the same periodicity and orientation, revealing intertwined electronic instabilities. Our findings not only validate a long-standing prediction but also establish layer-engineered van der Waals materials as a versatile platform for exploring and controlling hybridization-driven quantum phases.

cond-mat.str-el↗

arXiv2026

Lung Nodule Image Synthesis Driven by Two-Stage Generative Adversarial Networks

The limited sample size and insufficient diversity of lung nodule CT datasets severely restrict the performance and generalization ability of detection models. Existing methods generate images with insufficient diversity and controllability, suffering from issues such as monotonous texture features and distorted anatomical structures. Therefore, we propose a two-stage generative adversarial network (TSGAN) to enhance the diversity and spatial controllability of synthetic data by decoupling the morphological structure and texture features of lung nodules. In the first stage, StyleGAN is used to generate semantic segmentation mask images, encoding lung nodules and tissue backgrounds to control the anatomical structure of lung nodule images; The second stage uses the DL-Pix2Pix model to translate the mask map into CT images, employing local importance attention to capture local features, while utilizing dynamic weight multi-head window attention to enhance the modeling capability of lung nodule texture and background. Compared to the original dataset, the accuracy improved by 4.6% and mAP by 4% on the LUNA16 dataset. Experimental results demonstrate that TSGAN can enhance the quality of synthetic images and the performance of detection models.

cs.CV↗

arXiv2025

Two-Dimensional Altermagnetism in Epitaxial CrSb Ultrathin Films

Altermagnets constitute an emerging class of collinear magnets that exhibit zero net magnetization yet host spin-split electronic bands arising from non-relativistic spin-space-group symmetries. Realization of altermagnetism in the two-dimensional (2D) limit remains an outstanding challenge because dimensional reduction suppresses kZ dispersion and destabilizes the symmetry operations essential for spin compensation. Here, we investigate ultrathin CrSb films grown epitaxially on Bi2Te3 substrate and uncover the evolution of altermagnetism in the 2D limit. Scanning tunneling microscopy (STM), quasiparticle interference (QPI), angle-resolved photoemission spectroscopy (ARPES), and density functional theory (DFT) calculations show that interfacial symmetry breaking in the one-unit-cell (1 UC) limit gives rise to localized electronic states and uncompensated magnetic moments. These interfacial effects become weakened from 7/4 UC, accompanied by the recovery of a bulk-like coordination environment and the emergence of altermagnetic electronic characteristics. Our results show that the essential altermagnetic electronic structure of CrSb survives at a thickness of only ~1.05 nm, demonstrating the robustness of altermagnetism in the 2D limit and opening opportunities for integrating stray-field-free spin order into low dimensional spintronic architectures.

cond-mat.mtrl-sci↗

arXiv2025

Signature of gate tunable superconducting network in twisted bilayer graphene

Twisted van der Waals materials provide a tunable platform for investigating two-dimensional superconductivity and quantum phases. Using spectra-imaging scanning tunneling microscopy, we study the superconducting states in twisted bilayer graphene and track their evolution from insulating phases. Gate-dependent spectroscopic measurements reveal two distinct regimes: under-doped ({\nu} = -2.3) and optimally doped ({\nu} = -2.6). In the under-doped regime, partial superconductivity arises, forming a network interspersed with non-gapped regions. At optimal doping, the entire unit cell demonstrates superconductivity, with gap size modulation showing an anti-correlation with the local density of states. This gate-dependent transition from an insulating phase to a modulated superconductor uncovers an unexpected spatial hierarchy in pairing behavior and offers direct microscopic insights to constrain theories of superconductivity in moir\'e systems.

cond-mat.supr-con↗

arXiv2025

Towards Unified Neural Decoding with Brain Functional Network Modeling

Recent achievements in implantable brain-computer interfaces (iBCIs) have demonstrated the potential to decode cognitive and motor behaviors with intracranial brain recordings; however, individual physiological and electrode implantation heterogeneities have constrained current approaches to neural decoding within single individuals, rendering interindividual neural decoding elusive. Here, we present Multi-individual Brain Region-Aggregated Network (MIBRAIN), a neural decoding framework that constructs a whole functional brain network model by integrating intracranial neurophysiological recordings across multiple individuals. MIBRAIN leverages self-supervised learning to derive generalized neural prototypes and supports group-level analysis of brain-region interactions and inter-subject neural synchrony. To validate our framework, we recorded stereoelectroencephalography (sEEG) signals from a cohort of individuals performing Mandarin syllable articulation. Both real-time online and offline decoding experiments demonstrated significant improvements in both audible and silent articulation decoding, enhanced decoding accuracy with increased multi-subject data integration, and effective generalization to unseen subjects. Furthermore, neural predictions for regions without direct electrode coverage were validated against authentic neural data. Overall, this framework paves the way for robust neural decoding across individuals and offers insights for practical clinical applications.

q-bio.NC↗

arXiv2025

Petri Net Modeling of Root Hair Response to Phosphate Starvation in Arabidopsis Thaliana

Limited availability of inorganic phosphate (Pi) in soil is an important constraint to plant growth. In order to understand better the underlying mechanism of plant response to Pi, the response to phosphate starvation in Arabidopsis thaliana was investigated through use of Petri Nets, a formal language suitable for bio-modeling. A. thaliana displays a range of responses to deal with Pi starvation, but special attention was paid to root hair elongation in this study. A central player in the root hair pathway is the transcription factor ROOT HAIR DEFECTIVE 6-LIKE 4 (RSL4), which has been found to be upregulated during the Pi stress. A Petri Net was created which could simulate the gene regulatory networks responsible for the increase in root hair length, as well as the resulting increase in root hair length. Notably, discrepancies between the model and the literature suggested an important role for RSL2 in regulating RSL4. In the future, the net designed in the current study could be used as a platform to develop hypotheses about the interaction between RSL2 and RSL4.

q-bio.QM↗

arXiv2025

Modeling HIF-ILK Interaction Using Continuous Petri Nets

Oxygen concentration in tumor micro-environment is a well-established signal that can induce aggressive cancer behaviour. In particular, low oxygen levels (hypoxia) activate the Hypoxia-Inducible Factor(HIF) pathway which has an array of target systems. One of these systems is Integrin-Linked Kinase (ILK) pathway, which influences key signaling pathways for cell survival, proliferation, and migration. Hence, this paper aimed to explore the interconnection between these two pathways. Using the Petri net modeling tool Snoopy, an established HIF network model was transformed to be a continuous Petri net. Subsequently, the network was expanded to incorporate a feedback element from the ILK pathway to HIF, based on gene expression data. The resulting model conserved the oxygen switch response of the original HIF model and positively amplified HIF's output. Therefore, this model provides a starting point for establishing a system reflecting crucial effect on hypoxia-induced cancer behavior, and could potentially serve as a basis for future drug development.

q-bio.QM↗

arXiv2025

Analysis of 3D Urticaceae Pollen Classification Using Deep Learning Models

Due to the climate change, hay fever becomes a pressing healthcare problem with an increasing number of affected population, prolonged period of affect and severer symptoms. A precise pollen classification could help monitor the trend of allergic pollen in the air throughout the year and guide preventive strategies launched by municipalities. Most of the pollen classification works use 2D microscopy image or 2D projection derived from 3D image datasets. In this paper, we aim at using whole stack of 3D images for the classification and evaluating the classification performance with different deep learning models. The 3D image dataset used in this paper is from Urticaceae family, particularly the genera Urtica and Parietaria, which are morphologically similar yet differ significantly in allergenic potential. The pre-trained ResNet3D model, using optimal layer selection and extended epochs, achieved the best performance with an F1-score of 98.3%.

cs.CV↗

arXiv2024

Flavor Physics at the CEPC: a General Perspective

We discuss the landscape of flavor physics at the Circular Electron-Positron Collider (CEPC), based on the nominal luminosity outlined in its Technical Design Report. The CEPC is designed to operate in multiple modes to address a variety of tasks. At the $Z$ pole, the expected production of 4 Tera $Z$ bosons will provide unique and highly precise measurements of $Z$ boson couplings, while the substantial number of boosted heavy-flavored quarks and leptons produced in clean $Z$ decays will facilitate investigations into their flavor physics with unprecedented precision. We investigate the prospects of measuring various physics benchmarks and discuss their implications for particle theories and phenomenological models. Our studies indicate that, with its highlighted advantages and anticipated excellent detector performance, the CEPC can explore beauty and $\tau$ physics in ways that are superior to or complementary with the Belle II and Large-Hadron-Collider-beauty experiments, potentially enabling the detection of new physics at energy scales of 10 TeV and above. This potential also extends to the observation of yet-to-be-discovered rare and exotic processes, as well as testing fundamental principles such as lepton flavor universality, lepton and baryon number conservation, etc., making the CEPC a vibrant platform for flavor physics research. The $WW$ threshold scan, Higgs-factory operation and top-pair productions of the CEPC further enhance its merits in this regard, especially for measuring the Cabibbo-Kobayashi-Maskawa matrix elements, and Flavor-Changing-Neutral-Current physics of Higgs boson and top quarks. We outline the requirements for detector performance and considerations for future development to achieve the anticipated scientific goals.

hep-ex↗

arXiv2024

Towards Homogeneous Lexical Tone Decoding from Heterogeneous Intracranial Recordings

Recent advancements in brain-computer interfaces (BCIs) have enabled the decoding of lexical tones from intracranial recordings, offering the potential to restore the communication abilities of speech-impaired tonal language speakers. However, data heterogeneity induced by both physiological and instrumental factors poses a significant challenge for unified invasive brain tone decoding. Traditional subject-specific models, which operate under a heterogeneous decoding paradigm, fail to capture generalized neural representations and cannot effectively leverage data across subjects. To address these limitations, we introduce Homogeneity-Heterogeneity Disentangled Learning for neural Representations (H2DiLR), a novel framework that disentangles and learns both the homogeneity and heterogeneity from intracranial recordings across multiple subjects. To evaluate H2DiLR, we collected stereoelectroencephalography (sEEG) data from multiple participants reading Mandarin materials comprising 407 syllables, representing nearly all Mandarin characters. Extensive experiments demonstrate that H2DiLR, as a unified decoding paradigm, significantly outperforms the conventional heterogeneous decoding approach. Furthermore, we empirically confirm that H2DiLR effectively captures both homogeneity and heterogeneity during neural representation learning.

cs.CL↗

arXiv2024

Directly visualizing nematic superconductivity driven by the pair density wave in NbSe$_2$

Pair density wave (PDW) is a distinct superconducting state characterized by a periodic modulation of its order parameter in real space. Its intricate interplay with the charge density wave (CDW) state is a continuing topic of interest in condensed matter physics. While PDW states have been discovered in cuprates and other unconventional superconductors, the understanding of diverse PDWs and their interactions with different types of CDWs remains limited. Here, utilizing scanning tunneling microscopy, we unveil the subtle correlations between PDW ground states and two distinct CDW phases -- namely, anion-centered-CDW (AC-CDW) and hollow-centered-CDW (HC-CDW) -- in 2H-NbSe$_2$. In both CDW regions, we observe coexisting PDWs with a commensurate structure that aligns with the underlying CDW phase. The superconducting gap size, $\Delta(r)$, related to the pairing order parameter is in phase with the charge density in both CDW regions. Meanwhile, the coherence peak height, $H(r)$, qualitatively reflecting the electron-pair density, exhibits a phase difference of approximately $2\pi/3$ relative to the CDW. The three-fold rotational symmetry is preserved in the HC-CDW region but is spontaneously broken in the AC-CDW region due to the PDW state, leading to the emergence of nematic superconductivity.

cond-mat.supr-con↗

arXiv2024

Nematic Ising superconductivity with hidden magnetism in few-layer 6R-TaS2

In van der Waals heterostructures (vdWHs), the manipulation of interlayer stacking/coupling allows for the construction of customizable quantum systems exhibiting exotic physics. An illustrative example is the diverse range of states of matter achieved through varying the proximity coupling between two-dimensional (2D) quantum spin liquid (QSL) and superconductors within the TaS2 family. This study presents a demonstration of the intertwined physics of spontaneous rotational symmetry breaking, hidden magnetism, and Ising superconductivity in the three-fold rotationally symmetric, non-magnetic natural vdWHs 6R-TaS2. A distinctive phase emerges in 6R-TaS2 below a characteristic temperature (T*) of approximately 30 K, which is characterized by a remarkable set of features, including a giant extrinsic anomalous Hall effect (AHE), Kondo screening, magnetic field-tunable thermal hysteresis, and nematic magneto-resistance. At lower temperatures, a coexistence of nematicity and Kondo screening with Ising superconductivity is observed, providing compelling evidence of hidden magnetism within a superconductor. This research not only sheds light on unexpected emergent physics resulting from the coupling of itinerant electrons and localized/correlated electrons in natural vdWHs but also emphasizes the potential for tailoring exotic quantum states through the manipulation of interlayer interactions.

cond-mat.str-el↗

arXiv2024

Semileptonic $B$ decays from Belle and Belle II

This report summarizes recent measurements of the CKM matrix elements $|V_{cb}|$, $|V_{ub}|$, and lepton-flavor universality tests in semileptonic B decays from the Belle and Belle~II experiments. The decay branching factions $R(D^{*})$ and $R(X)$ are measured with the Belle~II early dataset and the results are found to be consistent with the Standard Model predictions. From a simultaneous measurement of untagged $B^0 \to \pi^{-} \ell^{+} \nu$ and $B^{+} \to \rho^0 \ell^{+} \nu$, the $|V_{ub}|$ value is determined incorporating external theoretical constraints on the decay form factors. The first measurement of the full angular coefficients for $B \to D^* \ell \nu$ decays at Belle provides comprehensive information for determining the decay form factors and $|V_{\mathrm{cb}}|$. This presentation also includes measurements of the ratios $|V_{ub}|^{\mathrm{excl}}/|V_{ub}|^{\mathrm{incl}}$ and $|V_{ub}|^{\mathrm{incl}}/|V_{cb}|^{\mathrm{incl}}$.

hep-ex↗

arXiv2024

Towards free-response paradigm: a theory on decision-making in spiking neural networks

The energy-efficient and brain-like information processing abilities of Spiking Neural Networks (SNNs) have attracted considerable attention, establishing them as a crucial element of brain-inspired computing. One prevalent challenge encountered by SNNs is the trade-off between inference speed and accuracy, which requires sufficient time to achieve the desired level of performance. Drawing inspiration from animal behavior experiments that demonstrate a connection between decision-making reaction times, task complexity, and confidence levels, this study seeks to apply these insights to SNNs. The focus is on understanding how SNNs make inferences, with a particular emphasis on untangling the interplay between signal and noise in decision-making processes. The proposed theoretical framework introduces a new optimization objective for SNN training, highlighting the importance of not only the accuracy of decisions but also the development of predictive confidence through learning from past experiences. Experimental results demonstrate that SNNs trained according to this framework exhibit improved confidence expression, leading to better decision-making outcomes. In addition, a strategy is introduced for efficient decision-making during inference, which allows SNNs to complete tasks more quickly and can use stopping times as indicators of decision confidence. By integrating neuroscience insights with neuromorphic computing, this study opens up new possibilities to explore the capabilities of SNNs and advance their application in complex decision-making scenarios.

cs.NE↗

arXiv2024

Unified Predefined-time Stability Conditions of Nonlinear Systems with Lyapunov Analysis

This brief gives a set of unified Lyapunov stability conditions to guarantee the predefined-time/finite-time stability of a dynamical systems. The derived Lyapunov theorem for autonomous systems establishes equivalence with existing theorems on predefined-time/finite-time stability. The findings proposed herein develop a nonsingular sliding mode control framework for an Euler-Lagrange system to analyze its stability, and its upper bound for the settling time can be arbitrarily determined a priori through predefined time constant.

eess.SY↗

arXiv2024

Modeling iPSC-derived Endothelial Cell Transition in Tumor Angiogenesis using Petri Nets

Tumor angiogenesis concerns the development of new blood vessels supplying the necessary nutrients for the further development of existing tumor cells. The entire process is complex, involving the production and consumption of chemicals, endothelial cell transitions as well as cell interactions, divisions, and migrations. Microfluidic cell culture platform has been used to study angiogenesis of endothelial cells derived from human induced pluripotent stem cells (iPSC-ECs) for a physiological relevant micro-environment. In this paper, we elaborate on how to define a pipeline for simulating the transformation and process that an iPSC-derived endothelial cell goes through in this biological scenario. We leverage the robustness and simplicity of Petri nets for modeling the cell transformation and associated constraints. The environmental and spacial factors are added using custom 2-dimensional grids. Although the pipeline does not capture the entire complexity of tumor angiogenesis, we are able to capture the essence of endothelial cell transitions in tumor angiogenesis using this conceptually simplified solution.

q-bio.CB↗