arXiv subjects

Zhan Wang

Publications and source records attributed to Zhan Wang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Discriminating Gap Symmetries of Superconducting La$_3$Ni$_2$O$_7$

The discovery of high-T$_c$ superconductor in Ruddlesden-Popper nickelate materials represented by La$_3$Ni$_2$O$_7$ has opened new directions in the quest for unconventional superconductivity. A central unresolved issue concerns the pairing symmetry of the superconducting order. In this paper, we model the superconducting order of La$_3$Ni$_2$O$_7$ using the established Fermi surface structure together with phenomenological pairing functions belonging to $s_\pm$ and $d$-wave symmetry classes, which are the leading possibilities in the current debate. We compute several experimentally accessible observables-including tunneling density of states, point contact spectroscopy, superfluid density, and Raman spectroscopy-each of which exhibits distinct characteristics for different gap symmetries. These quantities provide a concrete and experimentally testable route for identifying the pairing symmetry of La$_3$Ni$_2$O$_7$ and for clarifying the microscopic nature of nickelate superconductivity.

cond-mat.supr-con↗

arXiv2025

The parent state in kagome metals and superconductors: Chiral-nematic Fermi liquid state

The kagome metals and superconductors hosting rich correlated and topological electronic states have captivated quantum materials research. These states are triggered by an unconventional chiral charge density wave (CDW) wherein a chiral superconductivity emerges at low temperatures, yet the origin of this chiral CDW order, the parent state, is unresolved. Here, we report the discovery of a parent chiral-nematic Fermi liquid state in kagome metals and superconductors. We use spectroscopic-imaging scanning tunneling microscopy to study Ti-doped CsV3Sb5 where the CDW is suppressed, and find that multiorbital Fermi surfaces break all mirror reflections and exhibit handedness. We observe the chiral low-energy quasiparticle dispersions, providing direct evidence for a chiral-nematic electronic structure. We further observe the direct transition from the parent state to a chiral-nematic superconducting state. Moreover, in the samples with chiral CDW, we also observe the residual chiral-nematic QPI features, demonstrating that the CDW-triggered exotic states descend from the chiral-nematic Fermi liquid. Our findings not only provide a plausible chiral-nematic parent state, but also establish a new conceptual framework for exploring the emergence and consequences of such exotic quantum phases.

cond-mat.supr-con↗

arXiv2025

Gravitomagnetic-Hydrodynamics and Turbulence in Early Universe

The nonlinear coupling between spacetime geometry and matter in the early Universe remains a frontier in theoretical cosmology. By introducing a novel gravitomagnetic-hydrodynamic framework, we reveal a fundamental analogy between magnetohydrodynamics and the co-evolution of spacetime geometry and relativistic plasma. We demonstrate that, in high-energy environments such as the electroweak phase transition, the (newly defined) gravitomagnetic Reynolds number becomes large, signifying a strongly coupled system where the gravitomagnetic field could be frozen into the fluid. This coupling inevitably leads to the emergence of gravitational Alfvén waves and could drive a transition to turbulence involving the dynamics of spacetime itself. Our findings suggest that gravitomagnetic-hydrodynamic turbulence may leave imprints on the stochastic gravitational wave background, offering a new window into the nonlinear dynamics of the primordial Universe.

gr-qc↗

arXiv2025

Electronic structure of $A$V$_3$Sb$_5$ kagome metals

The kagome metals $A$V$_3$Sb$_5$ ($A=$ K, Cs, Rb) have become a fascinating materials platform following the discovery of many novel quantum states due to the interplay between electronic correlation, topology, and geometry. Understanding their physical origin requires constructing effective theories that capture the low-energy electronic structure and electronic interactions. While the band structure calculated by density functional theory (DFT) broadly agrees with experiments in the unbroken symmetry phase, the multiorbital nature challenges a proper understanding of the band structure and its description by tight-binding models. Here, we point out the unusual and puzzling properties of the DFT electronic structure, including the sublattice type of the van Hove singularities, the geometric shape of the Fermi surface, and the orbital content of the low-energy band dispersion, which cannot be described by the commonly used one-orbital or multiorbital kagome tight-binding models. We address these fundamental puzzles and develop an extended Slater-Koster formalism that can successfully resolve these issues. We discover the important role of site-symmetry and interorbital hopping structure and provide a concrete multiorbital tight-binding model description of the electronic structure for $A$V$_3$Sb$_5$ and the family of ``135'' compounds with other transition metals. This is a crucial step toward studying the effects of electron-electron interactions for the correlated and topological states in kagome metals and superconductors.

cond-mat.str-el↗

arXiv2025

One-Point energy correlator inside jets

In this work, we introduce a new jet observable, the one-point energy correlators (EC), designed to characterize the in-jet energy flow distribution by measuring energy deposition at a specific angle relative to the jet axis. Building upon the transverse momentum dependent physics, we aim for the EC to provide novel insights into jet substructure and offer a new approach to study TMD physics, particularly gluon transverse momentum dependent fragmentation functions (TMDFFs) which are notoriously difficult to extract. We obtain the factorization of the EC jet function within Soft-Collinear Effective Theory and leverage the framework of semi-inclusive TMD fragmenting jet functions. We resum large global logarithms and non-global logarithms (NGLs) and show that the normalized EC jet function exhibits significantly reduced dependence on the factorization scale and is primarily sensitive to the jet scale. Finally, after incorporating non-perturbative effects, we present numerical calculations up to NNLL accuracy for global logarithms and LL accuracy for NGLs, and we compare these predictions with PYTHIA 8 simulations.

hep-ph↗

arXiv2025

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis

Hepatic echinococcosis (HE) is a widespread parasitic disease in underdeveloped pastoral areas with limited medical resources. While CNN-based and Transformer-based models have been widely applied to medical image segmentation, CNNs lack global context modeling due to local receptive fields, and Transformers, though capable of capturing long-range dependencies, are computationally expensive. Recently, state space models (SSMs), such as Mamba, have gained attention for their ability to model long sequences with linear complexity. In this paper, we propose EAGLE, a U-shaped network composed of a Progressive Visual State Space (PVSS) encoder and a Hybrid Visual State Space (HVSS) decoder that work collaboratively to achieve efficient and accurate segmentation of hepatic echinococcosis (HE) lesions. The proposed Convolutional Vision State Space Block (CVSSB) module is designed to fuse local and global features, while the Haar Wavelet Transformation Block (HWTB) module compresses spatial information into the channel dimension to enable lossless downsampling. Due to the lack of publicly available HE datasets, we collected CT slices from 260 patients at a local hospital. Experimental results show that EAGLE achieves state-of-the-art performance with a Dice Similarity Coefficient (DSC) of 89.76%, surpassing MSVM-UNet by 1.61%.

eess.IV↗

arXiv2025

Roton Superconductivity from Loop-Current Chern Metal on the Kagome Lattice

Motivated by the evidence for time-reversal symmetry (TRS) breaking in nonmagnetic kagome metals AV3Sb5, a novel electronic order of persistent orbital loop-current (LC) has been proposed for the observed charge density wave (CDW) state. The LC order and its impact on the succeeding superconducting (SC) state are central to the new physics of the kagome materials. We show that the LC order fundamentally changes the pairing instability and the SC state, leading to an extraordinary topological superconductor, dubbed as a roton superconductor. In the single-orbital model on the kagome lattice, the LC-CDW state is a Chern metal near van Hove filling with a partially filled Chern band hosting 3 Chern Fermi pockets (CFPs). Cooper pairing of quasiparticles on the CFPs generates 3 SC components coupled by complex Josephson couplings induced by the TRS breaking LC. Due to the discrete quantum geometry associated with the 3-fold rotation and sublattice permutation, a small LC produces a large Josephson phase that drives the leading SC instability to the roton superconductor where the relative phases of the 3 SC components are locked at 120°, forming an emergent vortex-antivortex lattice with pair density modulations. Properties of the roton superconductor include topological chiral edge states carrying nonzero electric currents, fractional 1/3 vortex excitations and charge-6e Cooper pair triplets that are immune to the internal chiral phase fluctuations. We discuss these SC properties in connection to recent experimental evidence for TRS breaking chiral SC state in kagome superconductors, exhibiting pair density modulations, zero-field SC diode effect, and charge-6e flux quantization. These findings are also relevant for the interplay between the orbital-driven quantum anomalous Hall and SC states in other systems, e.g. the graphene and transition metal dichalcogenide based quantum materials.

cond-mat.str-el↗

arXiv2025

Physics-Informed Neural Networks for the Korteweg-de Vries Equation for Internal Solitary Wave Problem: Forward Simulation and Inverse Parameter Estimation

Physics-informed neural networks (PINNs) have emerged as a transformative framework for addressing operator learning and inverse problems involving the Korteweg-de Vries (KdV) equation for internal solitary waves. By integrating physical constraints with data-driven optimization, PINNs overcome the critical challenges of parameter unmeasurability in the KdV equation for internal solitary waves in two-layer fluid systems. This work addresses two problems: (1) Operator learning constructs a mapping from parameters to solutions, enabling wave evolution predictions from unknown parameters. Comparative studies demonstrate prediction errors as low as $10^{-4}$ when using 1000 training points. (2) Inverse problem solving leverages sparse and potentially noisy observational data with physics-regularized constraints to invert nonlinear coefficients successfully. Compared to conventional approaches, this end-to-end differentiable paradigm unifies operator learning and inverse problem-solving while overcoming mesh discretization errors and high-dimensional parameter space iteration costs. The method shows effectiveness for internal wave problems in stratified fluids, providing both accurate forward modeling and robust parameter inversion capabilities, even under noise.

physics.flu-dyn↗

arXiv2025

JingZhao: A Framework for Rapid NIC Prototyping in the Domain-Specific-Network Era

The network is becoming domain-specific, which requires on-demand design of the network protocols, as well as the microarchitecture of the NIC. However, to develop such a NIC is not that easy. Since the scissor gap between network speed and the growth of CPU frequency is expanding, most of the protocols need to be offloaded to hardware. The process of designing, verifying and optimizing a domain-specific NIC usually takes great effort, which hinders the rapid iteration of new protocols and algorithms. In this paper, we propose JingZhao, an open-sourced framework for NIC prototyping, which could be leveraged to rapidly implement and verify a domain-specific NIC. Using this framework, we implement a fully-functional RDMA NIC (RNIC). To the best of our knowledge, this represents the first open-source RDMA NIC solution with complete compatibility to the standard OFED communication library. The RNIC was also taped out using TSMC's 28nm process to validate our design. Our evaluation results show that new network functions can be easily integrated into the framework, and achieve nearly line-rate packet processing.

cs.NI↗

arXiv2025

Self-doped Molecular Mott Insulator for Bilayer High-Temperature Superconducting La3Ni2O7

The bilayer structure of recently discovered high-temperature superconducting nickelates La$_3$Ni$_2$O$_7$ provides a new platform for investigating correlation and superconductivity. Starting from a bilayer Hubbard model, we show that there is a molecular Mott insulator limit formed by the bonding band owing to Hubbard interaction $U$ and large interlayer coupling. This molecular Mott insulator becomes self-doped due to electrons transferred to the antibonding bands at a weaker interlayer coupling strength. The self-doped molecular Mott insulator is similar to the doped Mott insulator studied in cuprates. We propose La$_3$Ni$_2$O$_7$ to be a self-doped molecular Mott insulator, whose molecular Mott limit is formed by two nearly degenerate antisymmetric $d_{x^2-y^2}$ and $d_{z^2}$ orbitals. Partial occupation of higher energy symmetric $d_{x^2-y^2}$ orbital leads to self-doping, which may be responsible for high-temperature superconductivity in La$_3$Ni$_2$O$_7$. The effects of Hund's coupling $J_H$ on the low-energy spectra are also studied via exact diagonalization. The proposed low-energy theory for La$_3$Ni$_2$O$_7$ is found to be valid in a wide range of $U$ and $J_H$.

cond-mat.str-el↗

arXiv2025

Facilitating Video Story Interaction with Multi-Agent Collaborative System

Video story interaction enables viewers to engage with and explore narrative content for personalized experiences. However, existing methods are limited to user selection, specially designed narratives, and lack customization. To address this, we propose an interactive system based on user intent. Our system uses a Vision Language Model (VLM) to enable machines to understand video stories, combining Retrieval-Augmented Generation (RAG) and a Multi-Agent System (MAS) to create evolving characters and scene experiences. It includes three stages: 1) Video story processing, utilizing VLM and prior knowledge to simulate human understanding of stories across three modalities. 2) Multi-space chat, creating growth-oriented characters through MAS interactions based on user queries and story stages. 3) Scene customization, expanding and visualizing various story scenes mentioned in dialogue. Applied to the Harry Potter series, our study shows the system effectively portrays emergent character social behavior and growth, enhancing the interactive experience in the video story world.

cs.HC↗

arXiv2025

Scaling Neural-Network-Based Molecular Dynamics with Long-Range Electrostatic Interactions to 51 Nanoseconds per Day

Neural network-based molecular dynamics (NNMD) simulations incorporating long-range electrostatic interactions have significantly extended the applicability to heterogeneous and ionic systems, enabling effective modeling critical physical phenomena such as protein folding and dipolar surface and maintaining ab initio accuracy. However, neural network inference and long-range force computation remain the major bottlenecks, severely limiting simulation speed. In this paper, we target DPLR, a state-of-the-art NNMD package that supports long-range electrostatics, and propose a set of comprehensive optimizations to enhance computational efficiency. We introduce (1) a hardware-offloaded FFT method to reduce the communication overhead; (2) an overlapping strategy that hides long-range force computations using a single core per node, and (3) a ring-based load balancing method that enables atom-level task evenly redistribution with minimal communication overhead. Experimental results on the Fugaku supercomputer show that our work achieves a 37x performance improvement, reaching a maximum simulation speed of 51 ns/day.

cs.DC↗

arXiv2025

VizTA: Enhancing Comprehension of Distributional Visualization with Visual-Lexical Fused Conversational Interface

Comprehending visualizations requires readers to interpret visual encoding and the underlying meanings actively. This poses challenges for visualization novices, particularly when interpreting distributional visualizations that depict statistical uncertainty. Advancements in LLM-based conversational interfaces show promise in promoting visualization comprehension. However, they fail to provide contextual explanations at fine-grained granularity, and chart readers are still required to mentally bridge visual information and textual explanations during conversations. Our formative study highlights the expectations for both lexical and visual feedback, as well as the importance of explicitly linking these two modalities throughout the conversation. The findings motivate the design of VizTA, a visualization teaching assistant that leverages the fusion of visual and lexical feedback to help readers better comprehend visualization. VizTA features a semantic-aware conversational agent capable of explaining contextual information within visualizations and employs a visual-lexical fusion design to facilitate chart-centered conversation. A between-subject study with 24 participants demonstrates the effectiveness of VizTA in supporting the understanding and reasoning tasks of distributional visualization across multiple scenarios.

cs.HC↗

arXiv2025

Feature Engineering Approach to Building Load Prediction: A Case Study for Commercial Building Chiller Plant Optimization in Tropical Weather

In tropical countries with high humidity, air conditioning can account for up to 60% of a building's energy use. For commercial buildings with centralized systems, the efficiency of the chiller plant is vital, and model predictive control provides an effective strategy for optimizing operations through dynamic adjustments based on accurate load predictions. Artificial neural networks are effective for modelling nonlinear systems but are prone to overfitting due to their complexity. Effective feature engineering can mitigate this issue. While weather data are crucial for load prediction, they are often used as raw numerical inputs without advanced processing. Clustering features is a technique that can reduce model complexity and enhance prediction accuracy. Although previous studies have explored clustering algorithms for load prediction, none have applied them to multidimensional weather data, revealing a research gap. This study presents a cooling load prediction model that combines a neural network with Kalman filtering and K-means clustering. Applied to real world data from a commercial skyscraper in Singapore's central business district, the model achieved a 46.5% improvement in prediction accuracy. An optimal chiller sequencing strategy was also developed through genetic algorithm optimization of the predictive load, potentially saving 13.8% in energy. Finally, the study evaluated the integration of thermal energy storage into the chiller plant design, demonstrating potential reductions in capital and operational costs of 26% and 13%, respectively.

eess.SY↗

arXiv2025

Exciton Condensation in Landau Levels of Quantum Spin Hall Insulators

We theoretically study the quantum spin Hall insulator (QSHI) in a perpendicular magnetic field. In the noninteracting case, the QSHI with space inversion and/or uniaxial spin rotation symmetry undergoes a topological transition into a normal insulator phase at a critical magnetic field $B_{\rm c}$. The exciton condensation in the lowest Landau levels is triggered by Coulomb interactions in the vicinity of $B_{\rm c}$ at low temperature and spontaneously breaks the inversion and the spin rotation symmetries. We propose that the electron spin resonance spectroscopy with the ac magnetic field also aligned in the perpendicular direction can directly probe the exciton condensation order. Our results should apply to QSHIs such as the InAs/GaSb quantum wells and monolayer transition metal dichalcogenides.

cond-mat.mes-hall↗

arXiv2024

HES-UNet: A U-Net for Hepatic Echinococcosis Lesion Segmentation

Hepatic echinococcosis (HE) is a prevalent disease in economically underdeveloped pastoral areas, where adequate medical resources are usually lacking. Existing methods often ignore multi-scale feature fusion or focus only on feature fusion between adjacent levels, which may lead to insufficient feature fusion. To address these issues, we propose HES-UNet, an efficient and accurate model for HE lesion segmentation. This model combines convolutional layers and attention modules to capture local and global features. During downsampling, the multi-directional downsampling block (MDB) is employed to integrate high-frequency and low-frequency features, effectively extracting image details. The multi-scale aggregation block (MAB) aggregates multi-scale feature information. In contrast, the multi-scale upsampling Block (MUB) learns highly abstract features and supplies this information to the skip connection module to fuse multi-scale features. Due to the distinct regional characteristics of HE, there is currently no publicly available high-quality dataset for training our model. We collected CT slice data from 268 patients at a certain hospital to train and evaluate the model. The experimental results show that HES-UNet achieves state-of-the-art performance on our dataset, achieving an overall Dice Similarity Coefficient (DSC) of 89.21%, which is 1.09% higher than that of TransUNet. The project page is available at https://chenjiayan-qhu.github.io/HES-UNet-page.

eess.IV↗

arXiv2024

Obliquely interacting solitary waves and wave wakes in free-surface flows

This paper investigates the weakly nonlinear isotropic bi-directional Benney--Luke (BL) equation, which is used to describe oceanic surface and internal waves in shallow water, with a particular focus on soliton dynamics. Using the Whitham modulation theory, we derive the modulation equations associated with the BL equation that describe the evolution of soliton amplitude and slope. By analyzing rarefaction waves and shock waves within these modulation equations, we derive the Riemann invariants and modified Rankine--Hugoniot conditions. These expressions help characterize the Mach expansion and Mach reflection phenomena of bent and reverse bent solitons. We also derive analytical formulas for the critical angle and the Mach stem amplitude, showing that as the soliton speed is in the vicinity of unity, the results from the BL equation align closely with those of the Kadomtsev--Petviashvili (KP) equation. Corresponding numerical results are obtained and show excellent agreement with theoretical predictions. Furthermore, as a far-field approximation for the forced BL equation -- which models wave and flow interactions with local topography -- the modulation equations yield a slowly varying similarity solution. This solution indicates that the precursor wavefronts created by topography moving at subcritical or critical speeds take the shape of a circular arc, in contrast to the parabolic wavefronts observed in the forced KP equation.

nlin.PS↗

arXiv2024

Scaling Molecular Dynamics with ab initio Accuracy to 149 Nanoseconds per Day

Physical phenomena such as chemical reactions, bond breaking, and phase transition require molecular dynamics (MD) simulation with ab initio accuracy ranging from milliseconds to microseconds. However, previous state-of-the-art neural network based MD packages such as DeePMD-kit can only reach 4.7 nanoseconds per day on the Fugaku supercomputer. In this paper, we present a novel node-based parallelization scheme to reduce communication by 81%, then optimize the computationally intensive kernels with sve-gemm and mixed precision. Finally, we implement intra-node load balance to further improve the scalability. Numerical results on the Fugaku supercomputer show that our work has significantly improved the time-to-solution of the DeePMD-kit by a factor of 31.7x, reaching 149 nanoseconds per day on 12,000 computing nodes. This work has opened the door for millisecond simulation with ab initio accuracy within one week for the first time.

cs.DC↗