arXiv subjects

Tao Liu

Publications and source records attributed to Tao Liu.

At least 127 records · Page 7Linked to original sources

arXiv2025

Curve-based slicer for multi-axis DLP 3D printing

This paper introduces a novel curve-based slicing method for generating planar layers with dynamically varying orientations in digital light processing (DLP) 3D printing. Our approach effectively addresses key challenges in DLP printing, such as regions with large overhangs and staircase artifacts, while preserving its intrinsic advantages of high resolution and fast printing speeds. We formulate the slicing problem as an optimization task, in which parametric curves are computed to define both the slicing layers and the model partitioning through their tangent planes. These curves inherently define motion trajectories for the build platform and can be optimized to meet critical manufacturing objectives, including collision-free motion and floating-free deposition. We validate our method through physical experiments on a robotic multi-axis DLP printing setup, demonstrating that the optimized curves can robustly guide smooth, high-quality fabrication of complex geometries.

cs.GR↗

arXiv2025

$gg\rightarrow HH$ amplitude induced by bottom quarks at two-loop level: planar master integrals

We consider the two-loop amplitude of $gg\rightarrow HH$ mediated by bottom quarks, which provides a correction of percent level at leading order in the low invariant mass region. In order to compute the corresponding master integrals, we perform an expansion for the bottom quark mass, using the method of differential equations and fixing the boundary constants by a numerical approach. The results of 177 planar master integrals which are expressed in terms of multiple polylogarithms are provided.

hep-ph↗

arXiv2025

From Time-series Generation, Model Selection to Transfer Learning: A Comparative Review of Pixel-wise Approaches for Large-scale Crop Mapping

Crop mapping involves identifying and classifying crop types using spatial data, primarily derived from remote sensing imagery. This study presents the first comprehensive review of large-scale, pixel-wise crop mapping workflows, encompassing both conventional supervised methods and emerging transfer learning approaches. To identify the optimal time-series generation approaches and supervised crop mapping models, we conducted systematic experiments, comparing six widely adopted satellite image-based preprocessing methods, alongside eleven supervised pixel-wise classification models. Additionally, we assessed the synergistic impact of varied training sample sizes and variable combinations. Moreover, we identified optimal transfer learning techniques for different magnitudes of domain shift. The evaluation of optimal methods was conducted across five diverse agricultural sites. Landsat 8 served as the primary satellite data source. Labels come from CDL trusted pixels and field surveys. Our findings reveal three key insights. First, fine-scale interval preprocessing paired with Transformer models consistently delivered optimal performance for both supervised and transferable workflows. RF offered rapid training and competitive performance in conventional supervised learning and direct transfer to similar domains. Second, transfer learning techniques enhanced workflow adaptability, with UDA being effective for homogeneous crop classes while fine-tuning remains robust across diverse scenarios. Finally, workflow choice depends heavily on the availability of labeled samples. With a sufficient sample size, supervised training typically delivers more accurate and generalizable results. Below a certain threshold, transfer learning that matches the level of domain shift is a viable alternative to achieve crop mapping. All code is publicly available to encourage reproducibility practice.

cs.CV↗

arXiv2025

Atomic-Scale Heterogeneity of Hydrogen in Metal Hydrides Revealed by Electron Ptychography

Hydrogen plays critical roles in materials science, particularly for advancing technologies in hydrogen storage and phase manipulation, while also posing challenges like hydrogen embrittlement. Understanding its behavior, vital for improving material properties, requires precise determination of atomic-scale distribution-a persistent challenge due to hydrogen's weak electron scattering and high mobility, as well as the limitations of conventional transmission electron microscopy. We demonstrate that multislice electron ptychography (MEP) overcomes these constraints through three key advances: exceptional sensitivity for hydrogen occupancy, three-dimensional quantification, and picometer-level precision in atomic positioning. Experimentally, MEP resolves heterogeneous hydrogen distributions and quantifies hydrogen-induced lattice displacements with picometer precision in multi-principal-element alloy hydrides. This work demonstrates MEP as a transformative method for directly probing hydrogen atoms in solids, unlocking fundamental understanding of hydrogen's impact on material properties.

cond-mat.mtrl-sci↗

arXiv2025

Optics design of the Super Tau-Charm Facility collider rings

The Super Tau-Charm Facility (STCF), China's next-generation electron-positron collider, targets an unprecedented luminosity exceeding 5x10^34 cm^-2 s^-1 at a center-of-mass energy of 4 GeV. The implementation of a submillimeter vertical beta function at interaction point (< 1 mm) and crab-waist collision scheme in this low-energy regime introduces critical challenges through severe nonlinear effects that constrain dynamic aperture and degrade Touschek lifetime. To address these constraints, we propose a novel quasi-two-fold symmetric lattice design integrating several synergistic features: Linear optics optimization minimizing the H-invariant around the ring to maximize local momentum acceptance (LMA); Up to third-order of local chromaticity correction in the interaction region combined with second-order achromatic arc optics, enhancing off-momentum beam dynamics; Configured FODO arc structure with interleaved sextupole groups satisfying -I transformation, suppressing third-order geometric aberrations while optimizing Montague function distributions; Advanced final focus system integrating chromatic sextupoles, crab sextupoles, and strategically positioned octupoles to counteract final quadrupole fringe fields. Furthermore, we develop a multi-objective genetic algorithm using the in-house toolkit PAMKIT to simultaneously optimize 46 sextupole families, maximizing both dynamic aperture and momentum bandwidth. Optics performance is evaluated under error conditions with appropriate corrections, ensuring robust beam dynamics.

physics.acc-ph↗

arXiv2025

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 $τ$ 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↗

arXiv2025

Quantitatively predicting angle-resolved polarized Raman intensity of anisotropic layered materials

Angle-resolved polarized Raman (ARPR) spectroscopy provides insights into optical anisotropy and symmetry-related electron-photon/electron-phonon couplings of anisotropic layered materials (ALMs). However, since their discovery over ten years ago, ARPR responses in ALM flakes has exhibited a puzzling dependence on flake thickness, excitation wavelength, and dielectric environment, complicating their understanding and prediction. By taking black phosphorus (BP) (large than 20 nm) flakes and four-layer Td-WTe2 as examples, this study introduces intrinsic Raman tensors (Rint) and proposes strategies to predict the ARPR intensity profiles of thick and atomically-thin ALM flakes by considering birefringence, linear dichroism and multilayer interference inside multilayered structures with experimentally determined complex refractive indexes along in-plane axes and complex tensor elements of Rint for the corresponding phonon modes. The tensor elements of effective Raman tensors (Reff), which are directly linked to the polarization vectors of incident and scattered light outside the ALM surface, were derived to quantitatively predict ARPR intensity for these ALM flakes, showing intricate dependence on ALM thickness, dielectric substrates, and excitation wavelengths. This framework can be extended to other ALM flakes from atomically-thin layers to bulk limit, facilitating comprehensive prediction of their ARPR intensity regardless of layer-dependent electronic properties.

cond-mat.mes-hall↗

arXiv2025

Interaction-Induced Higher-Order Topological Insulator via Floquet Engineering

Higher-order topological insulators have attracted significant interest in both static single-particle and many-body lattice systems. While periodically driven (Floquet) higher-order topological phases have been explored at the single-particle level, the role of interactions in such systems remains less understood. In this paper, we extend previous studies by investigating interaction-induced higher-order topological phases through Floquet engineering. To achieve this, we construct an extended Bose-Hubbard model on a square lattice subjected to periodic driving. We demonstrate the emergence of interaction-induced normal Floquet second-order topological corner states for doublons (i.e., bound boson pairs) from a trivial phase, which exhibit robustness against disorder. Notably, beyond the normal phase, we reveal an interaction-induced anomalous Floquet second-order topological phase, where in-gap corner states of doublons emerge within the $π/T$ gap ($T$ being the driving period). Our model, accessible with state-of-the-art ultracold atom techniques, provides a platform for realizing interaction-driven higher-order topological phases uniquely enabled by periodic driving, with no direct counterparts in static or single-particle systems.

cond-mat.mes-hall↗

arXiv2025

Interaction-Induced Second-Order Skin Effect

In contrast to the conventional (first-order) non-Hermitian skin effect (NHSE) in a $d$-dimensional system with linear size $L$, the $n$th-order (higher-order) NHSE is characterized by skin modes localized at lower-dimensional boundaries of dimension $(d-n)$. The total number of these modes scales linearly with the system size $L$. Significant progress has been made in understanding higher-order NHSE in non-interacting systems. In this work, we demonstrate the many-body interaction induced second-order skin effect in a two-dimensional non-Hermitian bosonic system. Specifically, we construct a square lattice that incorporates nonreciprocal single-boson hopping, onsite many-body interactions and two-boson pairing hopping. In the absence of interactions, no second-order NHSE is observed. However, with the inclusion of interactions, we identify interaction-induced skin modes for in-gap doublon states (i.e., bound pairs of bosons) localized at the corners of the lattice, while the bulk doublon states remain extended. These corner-localized skin modes arise from the interplay between interaction-induced edge states, localized along one-dimensional boundaries, and the nonreciprocal hopping along these boundaries. Furthermore, the number of corner skin modes scales linearly with the system size, confirming the presence of second-order NHSE in this interacting system. Our findings introduce a novel approach to realizing higher-order skin effects by leveraging interactions.

quant-ph↗

arXiv2025

Relation-aware Hierarchical Prompt for Open-vocabulary Scene Graph Generation

Open-vocabulary Scene Graph Generation (OV-SGG) overcomes the limitations of the closed-set assumption by aligning visual relationship representations with open-vocabulary textual representations. This enables the identification of novel visual relationships, making it applicable to real-world scenarios with diverse relationships. However, existing OV-SGG methods are constrained by fixed text representations, limiting diversity and accuracy in image-text alignment. To address these challenges, we propose the Relation-Aware Hierarchical Prompting (RAHP) framework, which enhances text representation by integrating subject-object and region-specific relation information. Our approach utilizes entity clustering to address the complexity of relation triplet categories, enabling the effective integration of subject-object information. Additionally, we utilize a large language model (LLM) to generate detailed region-aware prompts, capturing fine-grained visual interactions and improving alignment between visual and textual modalities. RAHP also introduces a dynamic selection mechanism within Vision-Language Models (VLMs), which adaptively selects relevant text prompts based on the visual content, reducing noise from irrelevant prompts. Extensive experiments on the Visual Genome and Open Images v6 datasets demonstrate that our framework consistently achieves state-of-the-art performance, demonstrating its effectiveness in addressing the challenges of open-vocabulary scene graph generation. The code is available at: https://github.com/Leon022/RAHP

cs.CV↗

arXiv2025

Dissipation and Interaction-Controlled Non-Hermitian Skin Effects

Non-Hermitian skin effects (NHSEs) have recently been extensively studied at the single-particle level. When many-body interactions become dominant, novel non-Hermitian phenomena can emerge. In this work, we propose an experimentally accessible mechanism to induce and control NHSEs in interacting and reciprocal dissipative systems. We consider both 1D and 2D Bose-Hubbard lattices subject to staggered two-particle loss combined with synthetic magnetic flux and long-range hopping. When the two-particle loss is small, the bound eigenstates (e.g., doublons and triplons) are all localized at the same boundary due to the interplay between the magnetic flux and staggered two-particle loss. In contrast, for strong two-particle loss, the skin-mode localization direction of the bound particles is unexpectedly reversed. This reversal stems from the combined effect of the staggered two-particle loss, synthetic magnetic flux, and long-range hopping, through which virtual second-order and third-order hopping processes induce effectively strong nonreciprocal hopping of doublons. Our results open up a new avenue for exploring novel non-Hermitian phenomena in many-body systems.

cond-mat.quant-gas↗

arXiv2025

Rapid MRI-Based Synthetic CT Simulations for Precise tFUS Targeting

Accurate targeting is critical for the effectiveness of transcranial focused ultrasound (tFUS) neuromodulation. While CT provides accurate skull acoustic properties, its ionizing radiation and poor soft tissue contrast limit clinical applicability. In contrast, MRI offers superior neuroanatomical visualization without radiation exposure but lacks skull property mapping. This study proposes a novel, fully CT free simulation framework that integrates MRI-derived synthetic CT (sCT) with efficient modeling techniques for rapid and precise tFUS targeting. We trained a deep-learning model to generate sCT from T1-weighted MRI and integrated it with both full-wave (k-Wave) and accelerated simulation methods, hybrid angular spectrum (kWASM) and Rayleigh-Sommerfeld ASM (RSASM). Across five skull models, both full-wave and hybrid pipelines using sCT demonstrated sub-millimeter targeting deviation, focal shape consistency (FWHM ~3.3-3.8 mm), and <0.2 normalized pressure error compared to CT-based gold standard. Notably, the kW-ASM and RS-ASM pipelines reduced simulation time from ~3320 s to 187 s and 34 s respectively, achieving ~94% and ~90% time savings. These results confirm that MRI-derived sCT combined with innovative rapid simulation techniques enables fast, accurate, and radiation-free tFUS planning, supporting its feasibility for scalable clinical applications.

physics.med-ph↗

arXiv2025

Chiral-Extended Photon-Emitter Dressed States in Non-Hermitian Topological Baths

The interplay of quantum emitters and non-Hermitian structured baths has received increasing attention in recent years. Here, we predict unconventional quantum optical behaviors of quantum emitters coupled to a non-Hermitian topological bath, which is realized in a 1D Su-Schrieffer-Heeger photonic chain subjected to nonlocal dissipation. In addition to the Hermitian-like chiral bound states in the middle line gap and skin-mode-like hidden bound states inside the point gap, we identify peculiar in-gap chiral and extended photon-emitter dressed states. This is due to the competition of topological-edge localization and non-Hermitian skin-mode localization in combination with the non-Bloch bulk-boundary correspondence. Strikingly, dissipation can shape the wavefunction profile of the dressed state. Furthermore, when two emitters are coupled to the same bath, such in-gap dressed states can mediate the nonreciprocal long-range emitter-emitter interactions, with the interaction range limited only by the dissipation of the bath. Our work opens the door to further study rich quantum optical phenomena and exotic many-body physics utilizing quantum emitters coupled to non-Hermitian baths.

quant-ph↗

arXiv2025

VGAT: A Cancer Survival Analysis Framework Transitioning from Generative Visual Question Answering to Genomic Reconstruction

Multimodal learning combining pathology images and genomic sequences enhances cancer survival analysis but faces clinical implementation barriers due to limited access to genomic sequencing in under-resourced regions. To enable survival prediction using only whole-slide images (WSI), we propose the Visual-Genomic Answering-Guided Transformer (VGAT), a framework integrating Visual Question Answering (VQA) techniques for genomic modality reconstruction. By adapting VQA's text feature extraction approach, we derive stable genomic representations that circumvent dimensionality challenges in raw genomic data. Simultaneously, a cluster-based visual prompt module selectively enhances discriminative WSI patches, addressing noise from unfiltered image regions. Evaluated across five TCGA datasets, VGAT outperforms existing WSI-only methods, demonstrating the viability of genomic-informed inference without sequencing. This approach bridges multimodal research and clinical feasibility in resource-constrained settings. The code link is https://github.com/CZZZZZZZZZZZZZZZZZ/VGAT.

cs.CV↗

arXiv2025

CoATA: Effective Co-Augmentation of Topology and Attribute for Graph Neural Networks

Graph Neural Networks (GNNs) have garnered substantial attention due to their remarkable capability in learning graph representations. However, real-world graphs often exhibit substantial noise and incompleteness, which severely degrades the performance of GNNs. Existing methods typically address this issue through single-dimensional augmentation, focusing either on refining topology structures or perturbing node attributes, thereby overlooking the deeper interplays between the two. To bridge this gap, this paper presents CoATA, a dual-channel GNN framework specifically designed for the Co-Augmentation of Topology and Attribute. Specifically, CoATA first propagates structural signals to enrich and denoise node attributes. Then, it projects the enhanced attribute space into a node-attribute bipartite graph for further refinement or reconstruction of the underlying structure. Subsequently, CoATA introduces contrastive learning, leveraging prototype alignment and consistency constraints, to facilitate mutual corrections between the augmented and original graphs. Finally, extensive experiments on seven benchmark datasets demonstrate that the proposed CoATA outperforms eleven state-of-the-art baseline methods, showcasing its effectiveness in capturing the synergistic relationship between topology and attributes.

cs.LG↗

arXiv2025

Non-degenerate Ground State of the Spin-Boson Model under Abelian Diagonalization

By utilizing a unitary transformation, we derive the necessary and sufficient conditions for the degeneracy between the even- and odd-parity energy states of the spin-boson model (SBM). Employing the Rayleigh quotient of matrix algebra, we rigorously prove that the ground state energy of the SBM is lower than the systems lowest possible degenerate energy and possesses a definite parity. Based on the necessary and sufficient conditions for parity breaking, we provide an analytical expression for the parity-breaking critical value, which is closely related to the expansion order and computational accuracy. This expression reproduces the SBM phase diagram obtained by quantum Monte Carlo (QMC) and logarithmic discretization numerical renormalization group (NRG) methods. However, this phase diagram does not characterize the ground state of the system.

cond-mat.str-el↗

arXiv2025

Diverse dark matter haloes in Two-field Fuzzy Dark Matter

Fuzzy dark matter (FDM) is a compelling candidate for dark matter, offering a natural explanation for the structure of diffuse low-mass haloes. However, the canonical FDM model with a mass of $10^{-22}~{\rm eV}$ encounters challenges in reproducing the observed diversity of dwarf galaxies, except for possibly scenarios where strong galactic feedback is invoked. The introduction of multiple-field FDM can provide a potential resolution to this diversity issue. The theoretical plausibility of this dark matter model is also enhanced by the fact that multiple axion species with logarithmically-distributed mass spectrum exist as a generic prediction of string theory. In this paper, we consider the axiverse hypothesis and investigate non-linear structure formation in the two-field fuzzy dark matter (2FDM) model. Our cosmological simulation with an unprecedented resolution and self-consistent initial conditions reveals the diverse structures of dark matter haloes in the 2FDM model for the first time. Depending on the formation time and local tidal activities, late-time haloes can host solitons of nested cores or solitons of one dominant species.

astro-ph.CO↗

arXiv2025

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning

Offline reinforcement learning (RL) heavily relies on the coverage of pre-collected data over the target policy's distribution. Existing studies aim to improve data-policy coverage to mitigate distributional shifts, but overlook security risks from insufficient coverage, and the single-step analysis is not consistent with the multi-step decision-making nature of offline RL. To address this, we introduce the sequence-level concentrability coefficient to quantify coverage, and reveal its exponential amplification on the upper bound of estimation errors through theoretical analysis. Building on this, we propose the Collapsing Sequence-Level Data-Policy Coverage (CSDPC) poisoning attack. Considering the continuous nature of offline RL data, we convert state-action pairs into decision units, and extract representative decision patterns that capture multi-step behavior. We identify rare patterns likely to cause insufficient coverage, and poison them to reduce coverage and exacerbate distributional shifts. Experiments show that poisoning just 1% of the dataset can degrade agent performance by 90%. This finding provides new perspectives for analyzing and safeguarding the security of offline RL.

cs.LG↗