Showing 1–25 of 25 results for author: Liao, Z

Optimization dynamics of Transformer backflow neural quantum states for the two-dimensional Hubbard model

Authors: Zong-Yu Liao, Jia-Qi Wang, Rong-Qiang He, Zhong-Yi Lu

Abstract: Building on the multi-determinant Transformer backflow neural quantum state (NQS) ansatz and the associated multi-stage training workflow for the doped two-dimensional Hubbard model, we investigate how the optimization dynamics of the NQS depend on several key optimization and architectural hyperparameters. The workflow consists of neural-network backflow (NNB) initialization, supervised Transform… ▽ More Building on the multi-determinant Transformer backflow neural quantum state (NQS) ansatz and the associated multi-stage training workflow for the doped two-dimensional Hubbard model, we investigate how the optimization dynamics of the NQS depend on several key optimization and architectural hyperparameters. The workflow consists of neural-network backflow (NNB) initialization, supervised Transformer pre-training, and main energy optimization using the Moment-Adaptive ReConfiguration Heuristic (MARCH) within variational Monte Carlo. Using the doped $4\times4$ periodic Hubbard model at $U=8$ as a baseline, we examine how the update-norm threshold, Transformer width, number of determinant channels, and Monte Carlo batch size affect convergence. We find that a moderate update constraint improves the efficiency of MARCH optimization, larger Transformer width and more determinant channels improve the expressive capacity of the ansatz, and larger Monte Carlo batches reduce sampling noise in the update direction. We further test the same workflow at half filling, weaker interaction strength, open boundary conditions, and on a larger $8\times8$ doped lattice. These results identify practical optimization trends for Transformer backflow NQSs and highlight the balance between ansatz expressivity, MARCH update stability, and Monte Carlo sampling quality. △ Less

Submitted 16 July, 2026; originally announced July 2026.

Comments: 11 pages, 4 figures, 1 table

Möbius-like Real-Space Topology Reshapes Spectral Winding Topology in Hatano-Nelson Rings

Authors: Yekai Shen, Shuhang Chen, Zishun Liao, Zhipeng Li

Abstract: The spectral winding number serves as a bulk topological invariant in non-Hermitian systems, governing the emergence of skin modes and encoding the non-Hermitian bulk-boundary correspondence. However, most existing studies are built on conventional lattice geometries such as linear chains, rings, or planar arrays, leaving the role of real-space topological connectivity as an independent degree of… ▽ More The spectral winding number serves as a bulk topological invariant in non-Hermitian systems, governing the emergence of skin modes and encoding the non-Hermitian bulk-boundary correspondence. However, most existing studies are built on conventional lattice geometries such as linear chains, rings, or planar arrays, leaving the role of real-space topological connectivity as an independent degree of freedom largely unexplored. Here, we construct a Möbius ring system by cutting two parallel Hatano-Nelson (HN) rings and reconnecting them with a half-twist, without altering any local hopping parameter. This topological reconstruction transforms the periodic-boundary spectrum from two disjoint ellipses into a multi-petalled rose curve, and leads to distinct decay lengths for different eigenstates under open boundary conditions. Moreover, the spectral winding number can be driven through discrete winding-number jumps by tuning the coupling strength, with critical values obtained analytically. Our results demonstrate that real-space Möbius connectivity, mediated by the coupling strength, provides an independent and tunable foundation for the systematic control of non-Hermitian topology, with implications for the design of topological devices and sensing schemes. △ Less

Submitted 30 May, 2026; originally announced June 2026.

Comments: 3 pages,3 figures. Submitted to Light Conference 2026

Neural Refractive Index Primitives for Flame Field Reconstruction Using Background-Oriented Schlieren

Authors: Xinyi Lu, Wei Hu, Zizhou Liao, Zheng Wang, Yue Zhang, Jingxuan Li

Abstract: An improved neural refractive-index-primitive method for background-oriented schlieren tomography is presented, enabling continuous three-dimensional reconstruction of refractive-index fields using a compact multilayer perceptron. The method adopts the refractive-index field as the sole neural primitive and integrates multiresolution hash encoding, automatic-discrete gradient losses, and a three-d… ▽ More An improved neural refractive-index-primitive method for background-oriented schlieren tomography is presented, enabling continuous three-dimensional reconstruction of refractive-index fields using a compact multilayer perceptron. The method adopts the refractive-index field as the sole neural primitive and integrates multiresolution hash encoding, automatic-discrete gradient losses, and a three-dimensional mask to enable fast convergence and high-resolution, spatially coherent reconstructions. Tests on numerical combustion phantoms and real flame data demonstrate accurate recovery of both large-scale structures and fine-scale turbulence, strong robustness to noise, and clear advantages over frequency-encoding-based and voxel-based reconstruction methods. △ Less

Submitted 11 May, 2026; originally announced May 2026.

Journal ref: Combustion and Flame, 2016

An efficient framework for quantum dynamics driven by nonclassical light

Authors: Sheng-Wen Li, Zeyang Liao, Mao-Xin Liu

Abstract: Understanding quantum system dynamics driven by nonclassical light pulses is challenging, particularly for general light states with large photon numbers. Here we introduce an efficient framework that makes this task tractable. By introducing a pulse-shaped P-representation, the exact quantum evolution is decomposed into a mixture of many independent quasi-classical branches, each governed by a st… ▽ More Understanding quantum system dynamics driven by nonclassical light pulses is challenging, particularly for general light states with large photon numbers. Here we introduce an efficient framework that makes this task tractable. By introducing a pulse-shaped P-representation, the exact quantum evolution is decomposed into a mixture of many independent quasi-classical branches, each governed by a standard master equation with a classical pulse which can be solved efficiently. As an illustration, for a two-level system interacting with an exponential pulse, we first find out the exact analytical solutions to the Bloch equations in each quasi-classical branch, and then by taking proper P-function average over all branches, the full system dynamics driven by nonclassical light pulses is analytically obtained. For the one-photon and two-photon cases, our method well reproduces the previous exact results either analytically or numerically. Crucially, our approach scales efficiently to more complex light states (Fock, thermal, squeezed vacuum states) with large photon numbers ($N\sim 100$). We further provide a clear physical interpretation how the system dynamics is influenced through the high-order optical coherence of the nonclassical pulses. This work provides a unified and computationally efficient route and a useful starting point to explore more complex quantum dynamics driven by nonclassical light in quantum optics and quantum information processing. △ Less

Submitted 24 April, 2026; originally announced April 2026.

Comments: 14 pages, 3 figures, comments are welcome

Bi-level Multi-criteria Optimization for Risk-informed Radiotherapy

Authors: Mara Schubert, Katrin Teichert, Zhongxing Liao, Thomas Bortfeld, Ali Ajdari

Abstract: In radiation therapy (RT) treatment planning, multi-criteria optimization (MCO) supports efficient plan selection but is usually solved for population-based dosimetric criteria and ignores patient-specific biological risk, potentially compromising outcomes in high-risk patients. We propose risk-guided MCO, a one-shot method that embeds a clinical risk model into conventional MCO, enabling interact… ▽ More In radiation therapy (RT) treatment planning, multi-criteria optimization (MCO) supports efficient plan selection but is usually solved for population-based dosimetric criteria and ignores patient-specific biological risk, potentially compromising outcomes in high-risk patients. We propose risk-guided MCO, a one-shot method that embeds a clinical risk model into conventional MCO, enabling interactive navigation between dosimetric and biological endpoints. The proposed algorithm uses a special order relation to fuse the classical MCO sandwiching algorithm with bi-level optimization, restricting the Pareto set to plans that achieve improvement in the secondary risk objective for user-defined, acceptable loss in primary clinical objectives. Thus, risk-guided MCO generates risk-optimized counterparts of clinical plans in a single run rather than by sequential or lexicographic planning. To assess the performance, we retrospectively analyzed 19 lung cancer patients treated with RT. The endpoint was the risk of grade 2+ radiation pneumonitis (RP), modeled using bootstrapped stepwise logistics regression with interaction terms, including baseline lung function, smoking history, and dosimetric factors. The risk-guided plans yielded a mean reduction of 8.0% in total lung V20 and 9.5% in right lung V5, translating into an average RP risk reduction of 7.7% (range=0.3%-20.1%), with small changes in target coverage (mean -1.2 D98[%] for CTV) and modest increase in heart dose (mean +1.74 Gy). This study presents the first proof-of-concept for integrating biological risk models directly within multi-criteria RT planning, enabling an interactive balance between established population-wide dose protocols and individualized outcome prediction. Our results demonstrate that the risk-informed MCO can reduce the risk of RP while maintaining target coverage. △ Less

Submitted 8 January, 2026; originally announced January 2026.

Universal scaling laws of boundary-driven turbulence

Authors: Yong-Ying Zeng, Zi-Ju Liao, Jun-Yi Li, Wei-Dong Su

Abstract: Turbulence is a fundamental flow phenomenon, typically anisotropic at large scales and approximately isotropic at small scales. The classical Kolmogorov scaling laws (2/3, -5/3 and 4/5) have been well-established for turbulence without small-scale body forcing, describing second-order velocity structure functions, energy spectra, and third-order velocity structure functions in an intermediate smal… ▽ More Turbulence is a fundamental flow phenomenon, typically anisotropic at large scales and approximately isotropic at small scales. The classical Kolmogorov scaling laws (2/3, -5/3 and 4/5) have been well-established for turbulence without small-scale body forcing, describing second-order velocity structure functions, energy spectra, and third-order velocity structure functions in an intermediate small-scale range. However, their validity boundary remains unclear. Here, we identify new 1 and -2 scaling laws (replacing 2/3 and -5/3 laws) alongside the unchanged 4/5 law in the core region of boundary-driven turbulence, where energy is injected solely through viscous friction at moving boundaries. Local isotropy is recovered after high-pass filtering. Notably, odd-order velocity structure functions with and without absolute value exhibit distinct scaling exponents. A characteristic speed in the inertial range, derived from the constant ratio of third- to second-order structure functions, quantifies the time-averaged projectile speed at the bulk interface. Based on energy dissipation rate and the characteristic speed, a phenomenological framework for structure functions is developed together with a model for probability distributions of velocity increment at distinct small-scales. The universal scaling laws formulated can produce the full set of scaling exponents for low- and high-order velocity structure functions, including both the odd-orders' with and without absolute value, which are validated by direct numerical simulations and experimental datasets. △ Less

Submitted 7 April, 2025; originally announced April 2025.

Turbulence modulation in particle-laden channel flow: the particle inertial effects

Authors: Zi-Mo Liao, Feng-Hui Lin, Luoqin Liu, Nan-Sheng Liu, Xi-Yun Lu

Abstract: The particle inertial effects on turbulence modulation in particle-laden channel flow are investigated through four-way coupled point-particle direct numerical simulations, with the mass loading fixed at $0.6$ and friction Stokes number $St^+$ varying from $3$ to $300$. A full transition pathway is realized in sequence from a drag-enhanced to a drag-reduced flow regime, before asymptotically appro… ▽ More The particle inertial effects on turbulence modulation in particle-laden channel flow are investigated through four-way coupled point-particle direct numerical simulations, with the mass loading fixed at $0.6$ and friction Stokes number $St^+$ varying from $3$ to $300$. A full transition pathway is realized in sequence from a drag-enhanced to a drag-reduced flow regime, before asymptotically approaching the single-phase state as $St^+$ increases continuously up to 300. For the first time, a set of transport equations for the particle phase is derived analytically to interpret the inter-phase coupling, in the context of the point-based statistical description of particle-laden turbulence. By virtue of this, two dominant mechanisms are substantially identified and quantified: a positive, particle-induced extra transport, which decreases monotonically with $St^+$, and a negative, particle-induced extra dissipation, which depends non-monotonically on $St^+$. The coupling of these two mechanisms leads to a direct contribution of particle phase to the shear stress balance, turbulent kinetic energy, and Reynolds stress budgets. As a consequence, with the increase of particle inertia, the self-sustaining cycle of near-wall turbulence transitions from being augmented to being suppressed and, eventually, recovers to the single-phase situation. This gives rise to an indirect effect, manifested by the non-monotonic modification of Reynolds shear stress and turbulent production rate. Taken together, comprehensive interplays between particle-modified turbulent transport, particle-induced extra transport and dissipation are analyzed and summarized, providing a holistic physical picture composed of consistent interpretations of turbulence modulation. △ Less

Submitted 25 January, 2025; originally announced January 2025.

Adaptive Graded Denoising of Seismic Data Based on Noise Estimation and Local Similarity

Authors: Xueting Yang, Yong Li, Zhangquan Liao, Yingtian Liu, Junheng Peng

Abstract: Seismic data denoising is an important part of seismic data processing, which directly relate to the follow-up processing of seismic data. In terms of this issue, many authors proposed many methods based on rank reduction, sparse transformation, domain transformation, and deep learning. However, when the seismic data is noisy, complex and uneven, these methods often lead to over-denoising or under… ▽ More Seismic data denoising is an important part of seismic data processing, which directly relate to the follow-up processing of seismic data. In terms of this issue, many authors proposed many methods based on rank reduction, sparse transformation, domain transformation, and deep learning. However, when the seismic data is noisy, complex and uneven, these methods often lead to over-denoising or under-denoising. To solve this problems, we proposed a novel method called noise level estimation and similarity segmentation for graded denoising. Specifically, we first assessed the average noise level of the entire seismic data and denoised it using block matching and three-dimensional filtering (BM3D) methods. Then, the denoised data is contrasted with the residual using local similarity, pinpointing regions where noise levels deviate significantly from the average. The remaining data is retained intact. These areas are then re-evaluated and denoised. Finally, we integrated the data retained after the first denoising with the re-denoising data to get a complete and cleaner data. This method is verified on theoretical model and actual seismic data. The experimental results show that this method has a good effect on seismic data with uneven noise. △ Less

Submitted 24 August, 2024; originally announced August 2024.

Comments: This article has been submitted to geophysics

MSC Class: 86-10 ACM Class: I.4.4

The Nash-MTL-STCN Method For Prestack Three-Parameter Inversion

Authors: Yingtian Liu, Yong Li, Huating Li, Junheng Peng, Zhangquan Liao, Wen Feng

Abstract: Deep learning (DL) techniques have been widely used in prestack three-parameter inversion to address its ill-posed problems. Among these DL techniques, Multi-task learning (MTL) methods can simultaneously train multiple tasks, thereby enhancing model generalization and predictive performance. However, existing MTL methods typically adopt heuristic or non-heuristic approaches to jointly update the… ▽ More Deep learning (DL) techniques have been widely used in prestack three-parameter inversion to address its ill-posed problems. Among these DL techniques, Multi-task learning (MTL) methods can simultaneously train multiple tasks, thereby enhancing model generalization and predictive performance. However, existing MTL methods typically adopt heuristic or non-heuristic approaches to jointly update the gradient of each task, leading to gradient conflicts between different tasks and reducing inversion accuracy. To address this issue, we propose a semi-supervised temporal convolutional network (STCN) based on Nash equilibrium (Nash-MTL-STCN). Firstly, temporal convolutional networks (TCN) with non-causal convolution and convolutional neural networks (CNNs) are used as multi-task layers to extract the shared features from partial angle stack seismic data, with CNNs serving as the single-task layer. Subsequently, the feature mechanism is utilized to extract shared features in the multi-task layer through hierarchical processing, and the gradient combination of these shared features is treated as a Nash game for strategy optimization and joint updates. Ultimately, the overall utility of the three-parameter is maximized, and gradient conflicts are alleviated. In addition, to enhance the network's generalization and stability, we have incorporated geophysical forward modeling and low-frequency models into the network. Experimental results demonstrate that the proposed method overcomes the gradient conflict issue of the conventional MTL methods with constant weights (CW) and achieves higher precision than four widely used non-heuristic MTL methods. Further field data experiments also validate the method's effectiveness. △ Less

Submitted 18 March, 2025; v1 submitted 30 June, 2024; originally announced July 2024.

Comments: This manuscript has been submitted to Geophysics for reviewing

MSC Class: 86-10 ACM Class: I.2.6; I.6.4

An anti-noise seismic inversion method based on diffusion model

Authors: Yingtian Liu, Yong Li, Xingan Hao, Huating Li, Zhangquan Liao, Junheng Peng

Abstract: Seismic impedance inversion is one of the most important part of geophysical exploration. However, due to random noise, the traditional semi-supervised learning (SSL) methods lack generalization and stability. To solve this problem, some authors have proposed SSL methods with anti-noise function to improve noise robustness and inversion accuracy. However, such methods are often not ideal when face… ▽ More Seismic impedance inversion is one of the most important part of geophysical exploration. However, due to random noise, the traditional semi-supervised learning (SSL) methods lack generalization and stability. To solve this problem, some authors have proposed SSL methods with anti-noise function to improve noise robustness and inversion accuracy. However, such methods are often not ideal when faced with strong noise. In addition, Low-frequency impedance models can mitigate this problem, but creating accurate low-frequency models is difficult and error-prone when well-log data is sparse and subsurface structures are complex. To address those issues, we propose a novel deep learning inversion method called DSIM-USSL (Unsupervised and Semi-supervised joint Learning for Seismic Inversion based on diffusion model). Specifically, we are the first to introduce a diffusion model with strong noise tendency and construct a diffusion seismic inversion model (DSIM). In the reverse diffusion of DSIM, we design the encoder-decoder which combines CNN for capturing local features and GRU for global sequence modeling; and we choose U-net to learn the distribution of random noise, enhancing the generalization and stability of proposed method. Furthermore, to further improve generalization of the proposed method, a two-step training approach (USSL) is utilized. First, an unsupervised trained encoder-decoder is used as the initial network model in place of the traditional low-frequency wave impedance model that is difficult to accurately acquire. Then, the SSL is employed to further optimize the encoder-decoder model. Experimental results on the Marmousi2 model and field data demonstrate that the DSIM-USSL method achieves higher accuracy in the presence of seismic data with random noise, and maintains high stability even under strong noise conditions. △ Less

Submitted 8 May, 2024; originally announced May 2024.

On-liquid-gallium surface synthesis of ultra-smooth conductive metal-organic framework thin films

Authors: Jinxin Liu, Yunxu Chen, Xing Huang, Yanhan Ren, Mike Hambsch, David Bodesheim, Darius Pohl, Xiaodong Li, Marielle Deconinck, Bowen Zhang, Markus Löffler, Zhongquan Liao, Fengxiang Zhao, Arezoo Dianat, Gianaurelio Cuniberti, Yana Vaynzof, Junfeng Gao, Jingcheng Hao, Stefan C. B. Mannsfeld, Xinliang Feng, Renhao Dong

Abstract: Conductive metal-organic frameworks (MOFs) are emerging electroactive materials for (opto-)electronics. However, it remains a great challenge to achieve reliable MOF-based devices via the existing synthesis methods that are compatible with the complementary metal-oxide-semiconductor technology, as the surface roughness of thus-far synthetic MOF films or pellets is rather high for efficient electro… ▽ More Conductive metal-organic frameworks (MOFs) are emerging electroactive materials for (opto-)electronics. However, it remains a great challenge to achieve reliable MOF-based devices via the existing synthesis methods that are compatible with the complementary metal-oxide-semiconductor technology, as the surface roughness of thus-far synthetic MOF films or pellets is rather high for efficient electrode contact. Here, we develop an on-liquid-gallium surface synthesis (OLGSS) strategy under chemical vapor deposition (CVD) conditions for the controlled growth of two-dimensional conjugated MOF (2D c-MOF) thin films with ten-fold improvement of surface flatness (surface roughness can reach as low as ~2 Å) compared with MOF films grown by the traditional methods. Supported by theoretical modeling, we unveil a layer-by-layer CVD growth mode for constructing flattening surfaces, that is triggered by the high adhesion energy between gallium (Ga) and planar aromatic ligands. We further demonstrate the generality of the as-proposed OLGSS strategy by reproducing such a flat surface over nine different 2D c-MOF films with variable thicknesses (~2 to 208 nm) and large lateral sizes (over 1 cm2). The resultant ultra-smooth 2D c-MOF films enable the formation of high-quality electrical contacts with gold (Au) electrodes, leading to a reduction of contact resistance by over ten orders of magnitude compared to the traditional uneven MOF films. Furthermore, due to the efficient interfacial interaction benifited from the high-quality contacts, the prepared van der Waals heterostructure (vdWH) of OLGSS c-MOF and MoS2 exhibits intriguing photoluminescence (PL) enhancement, PL peak shift and large work function modulation. The establishment of the reliable OLGSS method provides the chances to push the development of MOF electronics and the construction of multicomponent MOF-based heterostructure materials. △ Less

Submitted 17 April, 2024; originally announced April 2024.

Fast Diffusion Model For Seismic Data Noise Attenuation

Authors: Junheng Peng, Yong Li, Yingtian Liu, Zhangquan Liao

Abstract: Noise is one of the primary sources of interference in seismic exploration. Many authors have proposed various methods to remove noise from seismic data; however, in the face of strong noise conditions, satisfactory results are often not achievable. In recent years, methods based on diffusion models have been applied to the task of strong noise processing in seismic data. However, due to iterative… ▽ More Noise is one of the primary sources of interference in seismic exploration. Many authors have proposed various methods to remove noise from seismic data; however, in the face of strong noise conditions, satisfactory results are often not achievable. In recent years, methods based on diffusion models have been applied to the task of strong noise processing in seismic data. However, due to iterative computations, the computational efficiency of diffusion-based methods is much lower than conventional methods. To address this issue, we propose using an improved Bayesian equation for iterations, removing the stochastic terms from the computation. Additionally, we proposed a new normalization method adapted to the diffusion model. Through various improvements, on synthetic datasets and field datasets, our proposed method achieves significantly better noise attenuation effects compared to the benchmark methods, while also achieving a several-fold increase in computational speed. We employ transfer learning to demonstrate the robustness of our proposed method on open-source synthetic seismic data and validate on open-source field data sets. Finally, we open-sourced the code to promote the development of high-precision and efficient seismic exploration work. △ Less

Submitted 3 April, 2024; originally announced April 2024.

Comments: This manuscript has been submitted to Geophysics for reviewing

MSC Class: 86-10 ACM Class: I.2.6; I.4.3

Seismic Data Strong Noise Attenuation Based on Diffusion Model and Principal Component Analysis

Authors: Junheng Peng, Yong Li, Zhangquan Liao, Xuben Wang, Xingyu Yang

Abstract: Seismic data noise processing is an important part of seismic exploration data processing, and the effect of noise elimination is directly related to the follow-up processing of data. In response to this problem, many authors have proposed methods based on rank reduction, sparse transformation, domain transformation, and deep learning. However, such methods are often not ideal when faced with stro… ▽ More Seismic data noise processing is an important part of seismic exploration data processing, and the effect of noise elimination is directly related to the follow-up processing of data. In response to this problem, many authors have proposed methods based on rank reduction, sparse transformation, domain transformation, and deep learning. However, such methods are often not ideal when faced with strong noise. Therefore, we propose to use diffusion model theory for noise removal. The Bayesian equation is used to reverse the noise addition process, and the noise reduction work is divided into multiple steps to effectively deal with high-noise situations. Furthermore, we propose to evaluate the noise level of blind Gaussian seismic data using principal component analysis to determine the number of steps for noise reduction processing of seismic data. We train the model on synthetic data and validate it on field data through transfer learning. Experiments show that our proposed method can identify most of the noise with less signal leakage. This has positive significance for high-precision seismic exploration and future seismic data signal processing research. △ Less

Submitted 10 September, 2023; originally announced September 2023.

Comments: 10 pages, 13 figures. This work has been submitted to the IEEE for possible publication

MSC Class: 86-10 ACM Class: I.2.6; I.4.3

Topologically protected subradiant cavity polaritons through linewidth narrowing enabled by dissipationless edge states

Authors: Yuwei Lu, Jingfeng Liu, Haoxiang Jiang, Zeyang Liao

Abstract: Cavity polaritons derived from the strong light-matter interaction at the quantum level provide a basis for efficient manipulation of quantum states via cavity field. Polaritons with narrow linewidth and long lifetime are appealing in applications such as quantum sensing and storage. Here, we propose a prototypical arrangement to implement a whispering-gallery-mode resonator with topological mirro… ▽ More Cavity polaritons derived from the strong light-matter interaction at the quantum level provide a basis for efficient manipulation of quantum states via cavity field. Polaritons with narrow linewidth and long lifetime are appealing in applications such as quantum sensing and storage. Here, we propose a prototypical arrangement to implement a whispering-gallery-mode resonator with topological mirror moulded by one-dimensional atom array, which allows to boost the lifetime of cavity polaritons over an order of magnitude. This considerable enhancement attributes to the coupling of polaritonic states to dissipationless edge states protected by the topological bandgap of atom array that suppresses the leakage of cavity modes. When exceeding the width of Rabi splitting, topological bandgap can further reduce the dissipation from polaritonic states to bulk states of atom array, giving arise to subradiant cavity polaritons with extremely sharp linewidth. The resultant Rabi oscillation decays with a rate even below the free-space decay of a single quantum emitter. Inheriting from the topologically protected properties of edge states, the subradiance of cavity polaritons can be preserved in the disordered atom mirror with moderate perturbations involving the atomic frequency, interaction strengths and location. Our work opens up a new paradigm of topology-engineered quantum states with robust quantum coherence for future applications in quantum computing and network. △ Less

Submitted 8 August, 2023; originally announced August 2023.

Comments: 19 pages,8 figures

Journal ref: Quantum Sci. Technol. 9,035019 (2024)

STCF Conceptual Design Report: Volume 1 -- Physics & Detector

Authors: M. Achasov, X. C. Ai, R. Aliberti, L. P. An, Q. An, X. Z. Bai, Y. Bai, O. Bakina, A. Barnyakov, V. Blinov, V. Bobrovnikov, D. Bodrov, A. Bogomyagkov, A. Bondar, I. Boyko, Z. H. Bu, F. M. Cai, H. Cai, J. J. Cao, Q. H. Cao, Z. Cao, Q. Chang, K. T. Chao, D. Y. Chen, H. Chen , et al. (413 additional authors not shown)

Abstract: The Super $τ$-Charm facility (STCF) is an electron-positron collider proposed by the Chinese particle physics community. It is designed to operate in a center-of-mass energy range from 2 to 7 GeV with a peak luminosity of $0.5\times 10^{35}{\rm cm}^{-2}{\rm s}^{-1}$ or higher. The STCF will produce a data sample about a factor of 100 larger than that by the present $τ$-Charm factory -- the BEPCII,… ▽ More The Super $τ$-Charm facility (STCF) is an electron-positron collider proposed by the Chinese particle physics community. It is designed to operate in a center-of-mass energy range from 2 to 7 GeV with a peak luminosity of $0.5\times 10^{35}{\rm cm}^{-2}{\rm s}^{-1}$ or higher. The STCF will produce a data sample about a factor of 100 larger than that by the present $τ$-Charm factory -- the BEPCII, providing a unique platform for exploring the asymmetry of matter-antimatter (charge-parity violation), in-depth studies of the internal structure of hadrons and the nature of non-perturbative strong interactions, as well as searching for exotic hadrons and physics beyond the Standard Model. The STCF project in China is under development with an extensive R\&D program. This document presents the physics opportunities at the STCF, describes conceptual designs of the STCF detector system, and discusses future plans for detector R\&D and physics case studies. △ Less

Submitted 5 October, 2023; v1 submitted 28 March, 2023; originally announced March 2023.

Journal ref: Front. Phys. 19(1), 14701 (2024)

Optical scattering imaging with sub-nanometer precision based on position-ultra-sensitive giant Lamb shift

Authors: Zeyang Liao, Yuwei Lu, Xue-Hua Wang

Abstract: The Lamb shift of a quantum emitter very close to a plasmonic nanostructure, mainly induced by the higher-order plasmonic dark modes, can be three or more orders of magnitude larger than that in the free space and it is ultra-sensitive to the emitter position and polarization. We show that this giant Lamb shift can be sensitively observed from the scattering spectrum dip shift of coupled system wh… ▽ More The Lamb shift of a quantum emitter very close to a plasmonic nanostructure, mainly induced by the higher-order plasmonic dark modes, can be three or more orders of magnitude larger than that in the free space and it is ultra-sensitive to the emitter position and polarization. We show that this giant Lamb shift can be sensitively observed from the scattering spectrum dip shift of coupled system when the plasmonic nanoparticle or tip scans through the emitter. Based on these observations, we propose an optical localization and polarization microscopy scheme with sub-nanometer precision for a quantum emitter via detecting the scattering spectrum instead of fluorescence. Our method is free of fluorescence quenching problem and it is relatively easier to be implemented in the plasmon-emitter coupling system. Moreover, the sample in our method does not need to be placed inside a plasmonic picocavity to enhance the radiative fluorescence rate and it also works even if the quantum emitter is slightly below a dielectric surface which can bring about broader applications in various fields, such as physics, chemistry, medicine, life science and materials science. △ Less

Submitted 4 June, 2023; v1 submitted 6 November, 2022; originally announced November 2022.

Journal ref: Sci. China Phys. Mech. Astron. 67, 264212 (2024)

Reduced-order variational mode decomposition

Authors: Zi-Mo Liao, Zhiye Zhao, Liang-Bing Chen, Zhen-Hua Wan, Nan-Sheng Liu, Xi-Yun Lu

Abstract: A novel data-driven method of modal analysis for complex flow dynamics, termed as reduced-order variational mode decomposition (RVMD), has been proposed, combining the idea of the separation of variables and a state-of-the-art nonstationary signal-processing technique -- variational mode decomposition. It enables a low-redundant adaptive extraction of coherent structures in statistically nonstatio… ▽ More A novel data-driven method of modal analysis for complex flow dynamics, termed as reduced-order variational mode decomposition (RVMD), has been proposed, combining the idea of the separation of variables and a state-of-the-art nonstationary signal-processing technique -- variational mode decomposition. It enables a low-redundant adaptive extraction of coherent structures in statistically nonstationary flows, with its modes computed by solving an elaborate optimization problem using the block coordinate descent algorithm. Discussion on the intrinsic relations between RVMD and some classic modal decomposition methods demonstrates that RVMD can be reduced into proper orthogonal decomposition (POD) or discrete Fourier transform (DFT) at particular parameter settings. The significant advantages of RVMD for performing time-frequency analysis are highlighted by a signal-processing analogous categorization of the widely-used modal decomposition techniques. It is also confirmed that the combination of RVMD and the Hilbert spectral analysis provides a physically intuitive way to explore the space-time-frequency characteristics of transient dynamics. Finally, all the appealing features of RVMD mentioned above are well verified via two canonical flow problems: the transient cylinder wake and the rectangular turbulent supersonic screeching jet. △ Less

Submitted 26 September, 2022; originally announced September 2022.

A metastable amorphous intermediate is responsible for laser-induced nucleation of glycine

Authors: Zhiyu Liao, Klaas Wynne

Abstract: Laser induced crystal nucleation through optical tweezing, and in particular polymorph selection through laser polarization, promises unprecedented control over crystallization. However, in the absence of a nearby liquid liquid critical point or miscibility gap, the origin of the required mesoscale clusters remains unclear. A number of recent studies of so called nonclassical nucleation have sugge… ▽ More Laser induced crystal nucleation through optical tweezing, and in particular polymorph selection through laser polarization, promises unprecedented control over crystallization. However, in the absence of a nearby liquid liquid critical point or miscibility gap, the origin of the required mesoscale clusters remains unclear. A number of recent studies of so called nonclassical nucleation have suggested the presence of large amorphous clusters. Here we show that supersaturated aqueous glycine solutions form metastable intermediate particles that are off the direct path to crystal nucleation. Laser induced crystal nucleation only occurs when the laser 'activates' one of these particles. In situ low frequency Raman spectroscopy is used to demonstrate their amorphous or partially ordered character and transformation to various crystal polymorphs. The requirement for solution aging in many previously reported laser-induced crystal nucleation experiments strongly suggests that the presence of amorphous intermediates is a general requirement. △ Less

Submitted 22 March, 2022; v1 submitted 21 October, 2021; originally announced October 2021.

Comments: 22 pages, 4 main figures and 12 supporting figures

Competing Phases in Epitaxial Vanadium Dioxide at Nanoscale

Authors: Yogesh Sharma, Martin V. Holt, Nouamane Laanait, Xiang Gao, Ilia Ivanov, Liam Collins, Changhee Sohn, Zhaoliang Liao, Elizabeth Skoropata, Sergei V. Kalinin, Nina Balke, Gyula Eres, Thomas Z. Ward, Ho Nyung Lee

Abstract: Phase competition in correlated oxides offers tantalizing opportunities as many intriguing physical phenomena occur near the phase transitions. Owing to a sharp metal-insulator transition (MIT) near room temperature, correlated vanadium dioxide (VO2) exhibits a strong competition between insulating and metallic phases that is important for practical applications. However, the phase boundary underg… ▽ More Phase competition in correlated oxides offers tantalizing opportunities as many intriguing physical phenomena occur near the phase transitions. Owing to a sharp metal-insulator transition (MIT) near room temperature, correlated vanadium dioxide (VO2) exhibits a strong competition between insulating and metallic phases that is important for practical applications. However, the phase boundary undergoes strong modification when strain is involved, yielding complex phase transitions. Here, we report the emergence of the nanoscale M2 phase domains in VO2 epitaxial films under anisotropic strain relaxation. The phase states of the films are imaged by multi-length-scale probes, detecting the structural and electrical properties in individual local domains. Competing evolution of the M1 and M2 phases indicates a critical role of lattice-strain on both the stability of the M2 Mott phase and the energetics of the MIT in VO2 films. This study demonstrates how strain engineering can be utilized to design phase states, which allow deliberate control of MIT behavior at the nanoscale in epitaxial VO2 films. △ Less

Submitted 13 August, 2019; originally announced August 2019.

Local Core Members Aided Community Structure Detection

Authors: Xiaoping Fan, Zhijie Chen, Fei Cai, Jinsong Wu, Shengzong Liu, Zhining Liao, Zhifang Liao

Abstract: The relationship of friends in social networks can be strong or weak. Some research works have shown that a close relationship between friends conducts good community structure. Based on this result, we propose an effective method in detecting community structure in social networks based on the closeness of relations among neighbors. This method calculates the gravity between each neighbor node to… ▽ More The relationship of friends in social networks can be strong or weak. Some research works have shown that a close relationship between friends conducts good community structure. Based on this result, we propose an effective method in detecting community structure in social networks based on the closeness of relations among neighbors. This method calculates the gravity between each neighbor node to core nodes, then makes judgement if the node should be classified in the community or not, and finally form the process of community detection. The experimental results show that the proposed method can mine the social structure efficiently with a low computational complexity △ Less

Submitted 31 October, 2017; originally announced October 2017.

Study on temperature coefficient of CdTe detector used for X-rays detection

Authors: Si-Ming Guo, Jin-Jie Wu, Jian Zhang, Xu-Fang Li, Cong-Zhan Liu, Shuai Zhang, Cheng-Ze Li, Bin-Bin Huo, Zhen-Yu Liao

Abstract: The temperature of the working environment is a key factor in determining the properties of semiconductor detectors, and it affects the absolute accuracy and stability of the standard detector. In order to determine the temperature coefficient of CdTe detector used for X-rays detection, a precise temperature control system was designed. In this experiment, detectors and radiographic source were se… ▽ More The temperature of the working environment is a key factor in determining the properties of semiconductor detectors, and it affects the absolute accuracy and stability of the standard detector. In order to determine the temperature coefficient of CdTe detector used for X-rays detection, a precise temperature control system was designed. In this experiment, detectors and radiographic source were set inside the thermostat with temperature of 0-40 Celsius degree, so that the temperature can be regulated for the test of the temperature coefficient of CdTe detector. Studies had shown that, with the increase of the temperature, the energy resolution and detection efficiency of the CdTe detector would deteriorate, and under 10 Celsius degree the detectors have better performance with the 8 keV X-rays. △ Less

Submitted 21 November, 2016; originally announced November 2016.

Comments: 8 pages, 7 figures

Spoof Plasmon Hybridization

Authors: Jingjing Zhang, Zhen Liao, Yu Luo, Xiaopeng Shen, Stefan A. Maier, Tie Jun Cui

Abstract: Plasmon hybridization between closely spaced nanoparticles yields new hybrid modes not found in individual constituents, allowing for the engineering of resonance properties and field enhancement capabilities of metallic nanostructure. Experimental verifications of plasmon hybridization have been thus far mostly limited to optical frequencies, as metals cannot support surface plasmons at longer wa… ▽ More Plasmon hybridization between closely spaced nanoparticles yields new hybrid modes not found in individual constituents, allowing for the engineering of resonance properties and field enhancement capabilities of metallic nanostructure. Experimental verifications of plasmon hybridization have been thus far mostly limited to optical frequencies, as metals cannot support surface plasmons at longer wavelengths. Here, we introduce the concept of 'spoof plasmon hybridization' in highly conductive metal structures and investigate experimentally the interaction of localized surface plasmon resonances (LSPR) in adjacent metal disks corrugated with subwavelength spiral patterns. We show that the hybridization results in the splitting of spoof plasmon modes into bonding and antibonding resonances analogous to molecular orbital rule and plasmonic hybridization in optical spectrum. These hybrid modes can be manipulated to produce enormous field enhancements (larger than 5000) by tuning the separation between disks or alternatively, the disk size, which effectively changes the relative gap size. The impact of the radiation loss is considered to find out the optimum disk size that maximizes field enhancement capabilities. Our investigation not only extends the range of applicability of the hybridization model, but also provides insightful guidance to exporting the exciting applications associated with plasmon hybridization to lower spectral range. △ Less

Submitted 12 October, 2016; originally announced October 2016.

Comments: 23 pages, 6 figures

Ultrafast Relaxation Dynamics of Photoexcited Dirac Fermion in The Three Dimensional Dirac Semimetal Cadmium Arsenide

Authors: Wei Lu, Shaofeng Ge, Xuefeng Liu, Hong Lu, Caizhen Li, Jiawei Lai, Chuan Zhao, Zhimin Liao, Shuang Jia, Dong Sun

Abstract: Three dimensional (3D) Dirac semimetals which can be seen as 3D analogues of graphene have attracted enormous interests in research recently. In order to apply these ultrahigh-mobility materials in future electronic/optoelectronic devices, it is crucial to understand the relaxation dynamics of photoexcited carriers and their coupling with lattice. In this work, we report ultrafast transient reflec… ▽ More Three dimensional (3D) Dirac semimetals which can be seen as 3D analogues of graphene have attracted enormous interests in research recently. In order to apply these ultrahigh-mobility materials in future electronic/optoelectronic devices, it is crucial to understand the relaxation dynamics of photoexcited carriers and their coupling with lattice. In this work, we report ultrafast transient reflection measurements of the photoexcited carrier dynamics in cadmium arsenide (Cd3As2), which is one of the most stable Dirac semimetals that have been confirmed experimentally. By using low energy probe photon of 0.3 eV, we probed the dynamics of the photoexcited carriers that are Dirac-Fermi-like approaching the Dirac point. We systematically studied the transient reflection on bulk and nanoplate samples that have different doping intensities by tuning the probe wavelength, pump power and lattice temperature, and find that the dynamical evolution of carrier distributions can be retrieved qualitatively by using a two-temperature model. This result is very similar to that of graphene, but the carrier cooling through the optical phonon couplings is slower and lasts over larger electron temperature range because the optical phonon energies in Cd3As2 are much lower than those in graphene. △ Less

Submitted 18 January, 2017; v1 submitted 26 August, 2016; originally announced August 2016.

Journal ref: Phys. Rev. B 95, 024303 (2017)

Raman spectroscopy and electrical properties of InAs nanowires with local oxidation enabled by substrate micro-trenches and laser irradiation

Authors: R. Tanta, M. H. Madsen, Z. Liao, P. Krogstrup, T. Vosch, J. Nygard, T. S. Jespersen

Abstract: The thermal gradient along indium-arsenide nanowires was engineered by a combination of fabricated micro- trenches in the supporting substrate and focused laser irradiation. This allowed local control of thermally activated oxidation reactions of the nanowire on the scale of the diffraction limit. The locality of the oxidation was detected by micro-Raman mapping, and the results were found consist… ▽ More The thermal gradient along indium-arsenide nanowires was engineered by a combination of fabricated micro- trenches in the supporting substrate and focused laser irradiation. This allowed local control of thermally activated oxidation reactions of the nanowire on the scale of the diffraction limit. The locality of the oxidation was detected by micro-Raman mapping, and the results were found consistent with numerical simulations of the temperature profile. Applying the technique to nanowires in electrical devices the locally oxidized nanowires remained conducting with a lower conductance as expected for an effectively thinner conducting core. △ Less

Submitted 25 January, 2016; originally announced January 2016.

Report number: NBI QDEV 2015

Journal ref: Appl. Phys. Lett. 107, 243101 (2015)

High-order localized spoof surface plasmon resonances and experimental verifications

Authors: Zhen Liao, Yu Luo, Antonio I. Fernández-Domínguez, Xiaopeng Shen, Stefan A. Maier, Tie Jun Cui

Abstract: We theoretically demonstrated and experimentally verified high-order radial spoof localized surface plasmon resonances supported by textured metal particles. Through an effective medium theory and exact numerical simulations, we show the emergence of these geometrically-originated electromagnetic modes at microwave frequencies. The occurrence of high-order radial spoof plasmon resonances is experi… ▽ More We theoretically demonstrated and experimentally verified high-order radial spoof localized surface plasmon resonances supported by textured metal particles. Through an effective medium theory and exact numerical simulations, we show the emergence of these geometrically-originated electromagnetic modes at microwave frequencies. The occurrence of high-order radial spoof plasmon resonances is experimentally verified in ultrathin disks. Their spectral and near-field properties are characterized experimentally, showing an excellent agreement with theoretical predictions. Our findings shed light into the nature of spoof localized surface plasmons, and open the way to the design of broadband plasmonic devices able to operate at very different frequency regimes. △ Less

Submitted 16 February, 2015; originally announced February 2015.

Comments: 29 pages, 10 figures