Showing 1–50 of 175 results for author: Xiao, Z

Dual-Faraday-laser-pumped cesium beam clock with $7.7\times 10^{-13}/\sqrtτ$ frequency stability

Authors: Xiaomin Qin, Suyang Wei, Haijun Chen, Yufei Yan, Qiang Wei, Hangbo Shi, Zhiyang Wang, Zheng Xiao, Zijie Liu, Tiantian Shi, Jingbiao Chen

Abstract: Compact cesium beam clocks are major frequency references for deployable timing systems. However, further improvement of their short-term frequency stability is limited by the clock signal-to-noise ratio (SNR). Although two-laser optical pumping can increase the effective atomic utilization, the achievable clock SNR has long been limited by laser-induced frequency-to-amplitude noise conversion. He… ▽ More Compact cesium beam clocks are major frequency references for deployable timing systems. However, further improvement of their short-term frequency stability is limited by the clock signal-to-noise ratio (SNR). Although two-laser optical pumping can increase the effective atomic utilization, the achievable clock SNR has long been limited by laser-induced frequency-to-amplitude noise conversion. Here, we demonstrate a compact dual-Faraday-laser-pumped (DFP) Cs beam clock enabled by a low-frequency-noise atom-referenced laser architecture. The intracavity Faraday anomalous dispersion optical filter provides inherent alignment to the Cs D$_2$ resonances, while modulation transfer spectroscopy offers suppressed frequency noise and drift. The resulting laser system supports robust turnkey operation with a Lorentzian linewidth of 2.12 kHz. The DFP Cs clock achieves a clock SNR of 46,365 in a 1-Hz bandwidth and a fractional Allan deviation of $7.7\times 10^{-13}/\sqrtτ$ , with Hadamard deviation reaching $7.7\times 10^{-15}$ at 10,000 s. This work pushes the fractional frequency stability of a compact Cs beam clock into the $10^{-13}/\sqrtτ$ regime, providing a pathway toward high-performance Cs frequency references for field-deployable precision timing, navigation, and synchronization. △ Less

Submitted 6 August, 2026; originally announced August 2026.

Comments: 12 pages, 5 figures

Photonic-chip-based generation of sub-100-femtosecond optical frequency combs

Authors: Weiqiang Xie, Zhengshun Lei, Zeyu Xiao, Yudi Zhao, Xing Zou, Wenqi Wei, Zihao Wang, Ting Wang, Jianjun Zhang, Bofang Zheng, Yikai Su

Abstract: Sub-100-fs optical pulses and frequency comb sources have been revolutionizing a wide range of applications, from ultrafast optical science to optical frequency standard and measurement. To date, the leading techniques for generating such pulses in practical systems rely on tabletop mode-locked lasers, which inherently suffer from high system complexity, limited long-term reliability, and pronounc… ▽ More Sub-100-fs optical pulses and frequency comb sources have been revolutionizing a wide range of applications, from ultrafast optical science to optical frequency standard and measurement. To date, the leading techniques for generating such pulses in practical systems rely on tabletop mode-locked lasers, which inherently suffer from high system complexity, limited long-term reliability, and pronounced environmental sensitivity. Meanwhile, driven by advances in photonic integration, chip-scale approaches have sought to realize miniaturized pulse sources. However, simultaneously achieving sub-100-fs duration, ideal pulse shape, and a broadband flat-topped spectrum remains a significant challenge. Here, we address these challenges by combining two key photonic chip technologies: TFLN EO modulators for picosecond seed pulse generation, and highly nonlinear optical loop mirrors (NOLM) based on AlGaAsOI nanowaveguides for efficient temporal pulse cleaning and spectral broadening. In theoretical simulation and experiment, we show that for an input seed pulse centred at ~1550nm, a single-stage AlGaAs NOLM with a loop length of 1cm can produce flat-topped, nearly tenfold spectral broadening and over tenfold compression of pulse width, and more than 10dB suppression of pulse pedestals. Using initial EO comb pulses with ps-level durations at repetition rates of 10-20GHz, we demonstrate photonic-chip-enabled pulses with an unprecedented duration of 55fs and a flat-topped comb spectrum whose 10dB optical bandwidth exceeds 90nm. Our results highlight the remarkable potential of photonic chip technologies to realize high-repetition-rate, miniaturized sub-100-fs optical pulse generators with the prospect of superior stability and operability. The demonstrated photonic-chip-based sub-100-fs optical frequency comb sources may establish a new paradigm for both scientific research and practical applications. △ Less

Submitted 5 August, 2026; originally announced August 2026.

Femtoscopy Measurement with S$π$RIT TPC in Radioactive BeamHeavy-ion Collisions

Authors: Y. J. Wang, C. K. Tam, Z. G. Xiao, W. G. Lynch, C. Y. Tsang, J. Barney, G. Jhang, J. Estee, M. B. Tsang, R. S. Wang, M. Kaneko, J. W. Lee, J. Park, Z. Chajęcki, G. Verde, T. Isobe, M. Kurata-Nishimura, T. Murakami, D. S. Ahn, L. Atar, T. Aumann, H. Baba, K. Boretzky, J. Brzychczyk, G. Cerizza , et al. (42 additional authors not shown)

Abstract: Femtoscopy is a powerful tool for exploring the dynamic emitting structure in heavy-ion collisions, while radioactive beam heavy-ion collisions enable the investigation of nuclear matter under extreme isospin conditions. Here, we successfully perform femtoscopy measurements using the S$π$RIT Time Projection Chamber (TPC). A dedicated correction scheme for track merging and splitting is proposed, w… ▽ More Femtoscopy is a powerful tool for exploring the dynamic emitting structure in heavy-ion collisions, while radioactive beam heavy-ion collisions enable the investigation of nuclear matter under extreme isospin conditions. Here, we successfully perform femtoscopy measurements using the S$π$RIT Time Projection Chamber (TPC). A dedicated correction scheme for track merging and splitting is proposed, which is well applicable to rectangular TPCs housed inside dipole magnets and effectively improves the reconstructed correlation functions at small relative momenta. Focusing on the proton-proton (p-p) correlation function in the 270 MeV/u $^{132}\text{Sn}+^{124}\text{Sn}$ system, we successfully apply the track merging and splitting correction; additionally, the TPC angular acceptance exhibits a negligible impact on the correlation function. A systematic uncertainty quantification framework is established. The experimental results of the p-p correlation function confirm the feasibility of the S$π$RIT TPC for femtoscopy measurements and provide technical support for high-precision femtoscopy studies using rectangular TPCs in radioactive beam heavy-ion collisions. △ Less

Submitted 15 July, 2026; originally announced July 2026.

Comments: 9 pages, 10 figures

Multi-Catheter Digitization in Brachytherapy via Few-Shot Synthetic-to-Real Learning and Structure-Aware Tracking

Authors: Zhuo Xiao, Bo Liu, Jingjing Wang, Qinglong Yao, Haitao Sun, Fugen Zhou, Junjie Wang, Qiuwen Wu, Ping Jiang

Abstract: Accurate catheter digitization in CT-guided interstitial brachytherapy is a critical but time-consuming task, especially for complex implant configurations. We developed a data-efficient, physics-guided framework for automated multi-catheter digitization with minimal clinical annotation. The pipeline consists of two stages. First, an implant region-aware network was pretrained on synthetic CT volu… ▽ More Accurate catheter digitization in CT-guided interstitial brachytherapy is a critical but time-consuming task, especially for complex implant configurations. We developed a data-efficient, physics-guided framework for automated multi-catheter digitization with minimal clinical annotation. The pipeline consists of two stages. First, an implant region-aware network was pretrained on synthetic CT volumes with simulated metallic signatures and then fine-tuned using only 10 clinical cases. Second, a structure-aware reconstruction module combined a direction-constrained 3D Hough transform with synchronous physics-constrained inward tracking to separate adherent catheter trajectories. The method was evaluated by patient-level five-fold cross-validation on 203 treatment fractions from 38 patients. The fine-tuned network achieved an HD95 of 0.853 +/- 0.362 mm. End-to-end evaluation yielded an F1 score of 0.891 +/- 0.178, with shaft and tip errors of 0.334 +/- 0.367 mm and 0.896 +/- 0.680 mm, respectively. In cases with severe catheter adhesion, the tracking F1 score remained 0.843 +/- 0.190. The complete workflow required approximately 11.6 s per case. These results indicate that combining few-shot synthetic-to-real learning with physics-guided structural tracking can provide robust and efficient multi-catheter digitization for time-sensitive clinical workflows. △ Less

Submitted 13 July, 2026; originally announced July 2026.

Comments: 9 figures and 2 tables

Laser-intensity-spike-dominated hot electron generation from two-plasmon decay instability driven by moderate-bandwidth pulses

Authors: C. Yao, Z. H. Cai, X. Wang, X. C. Wang, H. R. Yin, Z. A. Zhu, C. W. Lian, Y. Ji, X. Jiang, S. M. Xu, Y. Y. Yao, L. Y. Yang, J. N. Zhang, D. Meng, T. Peng, H. Wen, C. Z. Xiao, K. Y. Meng, J. Li, R. Yan, P. Yuan, Z. Zhang, L. Hao, Q. Jia, W. Feng , et al. (12 additional authors not shown)

Abstract: Our direct-drive-relevant experiments on the low-coherence Kunwu laser facility identify two-plasmon decay (TPD) as the primary source of hot electrons, and demonstrate for the first time that broadband laser pulses enhance TPD. Using particle-in-cell simulations, we attribute this TPD enhancement and the consequent hot electron production to stochastic intensity spikes inherent in broadband laser… ▽ More Our direct-drive-relevant experiments on the low-coherence Kunwu laser facility identify two-plasmon decay (TPD) as the primary source of hot electrons, and demonstrate for the first time that broadband laser pulses enhance TPD. Using particle-in-cell simulations, we attribute this TPD enhancement and the consequent hot electron production to stochastic intensity spikes inherent in broadband laser fields, robust in both weakly- and strongly-driven regimes. These findings suggest that mitigating hot electron generation requires suppressing these intensity spikes. △ Less

Submitted 24 June, 2026; originally announced June 2026.

NeuXtalViz: Interactive Three-Dimensional Visualization and Analysis for Single-Crystal Neutron Diffraction

Authors: Zachary Morgan, Zhongcan Xiao, Sylwia Pawledzio, Iris Ye, Kathleen Loughlin, Shiyun Jin, Vickie Lynch, Thomas Proffen, Christina Hoffmann, Xiaoping Wang

Abstract: NeuXtalViz (Neutron Single-Crystal Visualization) is a Python-based software package developed at Oak Ridge National Laboratory to provide interactive three-dimensional visualization and analysis tools for single-crystal neutron diffraction experiments. Built on the Mantid framework for data reduction, and leveraging PyVista and Matplotlib within a Python Qt environment, NeuXtalViz adopts a model-… ▽ More NeuXtalViz (Neutron Single-Crystal Visualization) is a Python-based software package developed at Oak Ridge National Laboratory to provide interactive three-dimensional visualization and analysis tools for single-crystal neutron diffraction experiments. Built on the Mantid framework for data reduction, and leveraging PyVista and Matplotlib within a Python Qt environment, NeuXtalViz adopts a model-view-presenter architecture that separates the user interface from the core processing components. The software provides a unified interface for tasks central to single-crystal diffraction, including UB-matrix determination, experiment planning, visualization of normalized reciprocal-space volumes, and real-space crystal-structure calculations. It also integrates with widely used community tools and has been deployed on instrument and analysis servers, where it is now being adopted by instrument teams and users. By embedding advanced three-dimensional visualization directly into the experimental workflow, NeuXtalViz enhances the planning, execution, and analysis cycle for single-crystal neutron diffraction experiments, while providing a flexible framework for future development. △ Less

Submitted 24 June, 2026; originally announced June 2026.

Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys

Authors: Zeyu Xia, Kan Ma, Sibo Cheng, Thomas Blackburn, Ziling Peng, Kewei Zhu, Weihang Zhang, Dunhui Xiao, Alexander J Knowles, Rossella Arcucci

Abstract: The performance of advanced materials for extreme environments is underpinned by their microstructure, including the size and distribution of reinforcing phases. Chromium-based superalloys are a recently proposed alternative to conventional face-centred-cubic superalloys for high-temperature applications, such as Concentrated Solar Power, and their development requires efficient measurement of pre… ▽ More The performance of advanced materials for extreme environments is underpinned by their microstructure, including the size and distribution of reinforcing phases. Chromium-based superalloys are a recently proposed alternative to conventional face-centred-cubic superalloys for high-temperature applications, such as Concentrated Solar Power, and their development requires efficient measurement of precipitate volume fraction and size distribution from electron microscopy images. Traditional fixed-threshold image processing is sensitive to background noise, generalises poorly across materials, and requires substantial manual measurement effort. To address these bottlenecks, this study proposes DT-SegNet, an end-to-end two-stage deep learning scheme based on YOLOv5 and SegFormer for object detection and segmentation in electron microscopy images. The approach combines the training efficiency of convolutional neural networks at the detection stage with the segmentation accuracy of a Vision Transformer. Numerical experiments show that DT-SegNet substantially outperforms state-of-the-art segmentation tools offered by Weka and ilastik across metrics including accuracy, precision, recall, and F1-score. The model provides a useful tool for alloy-development microstructure examinations and helps address the large datasets associated with high-throughput alloy development. △ Less

Submitted 20 June, 2026; originally announced June 2026.

Comments: 18 pages, 11 figures. Published in Phys. Chem. Chem. Phys

Journal ref: Phys. Chem. Chem. Phys. 25, 15970-15987 (2023)

FireDataForge: A Unified Framework for Multi-Source Wildfire Data Retrieval and Integration

Authors: Zeyu Xia, Lexie Chen, Ye Liu, Huilin Huang

Abstract: Wildfire research, modeling, and education require geospatial data from multiple sources that vary in formats, coordinate systems, spatial resolutions, and temporal cadences. This preprocessing burden limits reproducible reuse. We present FireDataForge, an open-source Python framework that automates retrieval and harmonization of 11 wildfire-related sources spanning fire behavior, weather, land co… ▽ More Wildfire research, modeling, and education require geospatial data from multiple sources that vary in formats, coordinate systems, spatial resolutions, and temporal cadences. This preprocessing burden limits reproducible reuse. We present FireDataForge, an open-source Python framework that automates retrieval and harmonization of 11 wildfire-related sources spanning fire behavior, weather, land cover, vegetation, elevation, built environment, wildland-urban interface, fire history, and satellite imagery. Given an MTBS Event ID, FireDataForge retrieves relevant datasets, aligns them to a common grid, and outputs analysis-ready NumPy arrays with embedded metadata. Batch processing of historical fires demonstrates support for fire behavior simulation, educational visualization, machine learning, and AI-assisted wildfire analysis. △ Less

Submitted 19 June, 2026; originally announced June 2026.

Comments: 5 pages, 2 figures. Accepted to the 2026 IEEE International Conference on Information Reuse and Integration for Data Science (IEEE IRI 2026)

Hyperon-Nucleon Spectrometer

Authors: Xiaozhi Bai, Xu Cao, Zhe Cao, Jinhui Chen, Kai Chen, Qibo Chen, Shi Chen, Xin Chen, Yuquan Chen, Zhenyu Chen, Jianping Dai, Heng-Tong Ding, Dongshuo Du, Shuxian Du, Limin Duan, Zhe Duan, Anhui Feng, Jie Feng, Yicheng Feng, Jinlin Fu, Xiaofeng Fu, Chaosong Gao, Liang Ge, Wenwen Ge, Lisheng Geng , et al. (215 additional authors not shown)

Abstract: Chirality lies at the heart of low-energy QCD, governing the symmetry structure that shapes hadron masses and strong interaction dynamics. Among the most compelling open questions tied to chiral dynamics and spontaneous chiral symmetry breaking is the longstanding $Λ$ polarization puzzle, in which $Λ$ hyperons produced in unpolarized hadronic collisions exhibit a surprisingly large transverse pola… ▽ More Chirality lies at the heart of low-energy QCD, governing the symmetry structure that shapes hadron masses and strong interaction dynamics. Among the most compelling open questions tied to chiral dynamics and spontaneous chiral symmetry breaking is the longstanding $Λ$ polarization puzzle, in which $Λ$ hyperons produced in unpolarized hadronic collisions exhibit a surprisingly large transverse polarization that remains theoretically unexplained. This whitepaper presents the proposal for the Hyperon-Nucleon Spectrometer (H-NS) at the High-Intensity heavy-ion Accelerator Facility (HIAF). Leveraging the high energy and high intensity of HIAF's proton and heavy-ion beams, the H-NS experiment will perform systematic studies of hyperon polarization phenomena and their underlying mechanisms in proton-proton ($pp$), proton-nucleus ($pA$), and nucleus-nucleus ($AA$) collisions in the fixed target mode. A wide-range beam energy scan, including proton beams from 3 GeV up to 9.3 GeV (HIAF) and up to 32 GeV (upgraded HIAF), will be conducted to examine the dependence of polarization on collision energy. The spectrometer is designed with specialized detectors capable of high-precision reconstruction of final-state baryon polarizations. Among its many interesting and important measurements, H-NS will simultaneously measure hyperon and proton spin observables to explore the polarization mechanism in hadronic interactions and the spin structure of baryons. Furthermore, the use of $pA$ and $AA$ collisions will enable detailed investigations of cold and hot nuclear matter effects on spin polarization. Its physics program and detector development will significantly benefit the future Electron-ion Collider in China. △ Less

Submitted 4 June, 2026; originally announced June 2026.

Comments: 69 pages, Hyperon-Nucleon Spectrometer (H-NS) white paper

High-Precision Ground Characterization of Test-Mass Magnetic Properties for the Taiji Gravitational Wave Mission via a Physics-Informed Neural Framework

Authors: Chang Liu, Qiong Deng, Huadong Li, Liwei Yang, Xiaodong Peng, Ziren Luo, Yuzhu Zhang, Chen Gao, Xiaotong Wei, Minghui Du, Zihao Xiao, Peng Xu, Bo Liang, Zhi Wang, Li-e Qiang

Abstract: Taiji is a gravitational wave detection mission in space initiated by the Chinese Academy of Sciences, which will open the millihertz window through a heliocentric triangular constellation of three drag-free spacecraft. Its ultimate sensitivity is determined partly by the residual acceleration noise of the gravitational reference sensors (GRS), within which the coupling between the test-mass and t… ▽ More Taiji is a gravitational wave detection mission in space initiated by the Chinese Academy of Sciences, which will open the millihertz window through a heliocentric triangular constellation of three drag-free spacecraft. Its ultimate sensitivity is determined partly by the residual acceleration noise of the gravitational reference sensors (GRS), within which the coupling between the test-mass and the fluctuating environmental magnetic field constitutes one of the key stray-force contributions. Following the path established by the LISA and TianQin teams, high-precision ground characterization of remanent magnetic moment $\vec{m}_r$ and volume susceptibility $χ$ of the test masses is a central step in the Taiji pre-launch test program. A persistent challenge for this characterization is the non-stationary, colored background noise inherent to torsion-pendulum facilities, which systematically biases classical Ordinary Least Squares (OLS) and Kalman filter (KF) estimators. We propose an AI-enhanced Differentiable Weighted Least Squares (AI-WLS) framework that fuses a dilated one-dimensional residual network, acting as a dynamic noise evaluator, with a fully differentiable analytical physical solver. This architecture preserves the exact linear mapping from the magnetic parameters to the torque response while autonomously identifying and suppressing contaminated data segments. Validated on real measured noise from the Changchun Institute of Optics, Fine Mechanics and Physics torsion-pendulum facility developed for Taiji, which achieves a torque sensitivity of order $10^{-13}\,\mathrm{N\cdot m\,Hz^{-1/2}}$, the AI-WLS framework bounds the maximum absolute estimation errors at $4.46\times 10^{-10}\,\mathrm{A\cdot m^2}$ for $\vec{m}_r$ and $7.8\times 10^{-8}$ for $χ$, satisfying Taiji's ground-test requirements on all these parameters simultaneously. △ Less

Submitted 25 April, 2026; originally announced April 2026.

Open dataset for benchmarking scaling laws of high-energy laser atmospheric propagation

Authors: Xusheng Xia, Zhilin Xia

Abstract: Scaling laws are increasingly used as fast surrogate models for high energy laser atmospheric propagation, yet their calibration and comparison still depend on large collections of high-fidelity wave-optics simulations. Existing studies usually rely on privately organized simulation outputs, which makes it difficult to reproduce published fits or evaluate new surrogate formulations on a shared ben… ▽ More Scaling laws are increasingly used as fast surrogate models for high energy laser atmospheric propagation, yet their calibration and comparison still depend on large collections of high-fidelity wave-optics simulations. Existing studies usually rely on privately organized simulation outputs, which makes it difficult to reproduce published fits or evaluate new surrogate formulations on a shared benchmark. We present a public simulation dataset for high energy laser atmospheric propagation with coupled turbulence and thermal blooming. The release contains 226,500 cases spanning target speed, emission geometry, aperture diameter, visibility, aerosol model, beam quality, turbulence strength, and laser power. Data are organized as a case-level main table linked to indexed long-exposure irradiance arrays and centralized metadata, which supports statistical analysis without hiding the underlying field outputs. The simulation pipeline is based on split-step wave-optics propagation with turbulence, attenuation, and thermal-blooming models that have been validated against established propagation references. The dataset is intended for scaling-law calibration, benchmark comparison, surrogate-model training, sensitivity analysis, and inverse studies. △ Less

Submitted 17 April, 2026; v1 submitted 14 April, 2026; originally announced April 2026.

Comments: 12 pages, 4 figures

ScatterPrism: convergence for generative simulation and inverse problems in particle and nuclear physics

Authors: Zeyu Xia, Tyler Kim, Trevor Reed, Judy Fox, Geoffrey Fox, Adam Szczepaniak

Abstract: High-fidelity simulations and complex inverse problems, such as detector modeling and unfolding, are computationally intensive bottlenecks across subatomic physics, yet essential for accurate physical interpretation. While Conditional Flow Matching (CFM) offers a robust acceleration approach, we demonstrate its standard training loss is fundamentally misleading. Specifically, utilizing a Jefferson… ▽ More High-fidelity simulations and complex inverse problems, such as detector modeling and unfolding, are computationally intensive bottlenecks across subatomic physics, yet essential for accurate physical interpretation. While Conditional Flow Matching (CFM) offers a robust acceleration approach, we demonstrate its standard training loss is fundamentally misleading. Specifically, utilizing a Jefferson Lab Nuclear Physics (NP) kinematic dataset ($γp \to ρ^0 p \to π^+π^- p$), we expose that CFM loss plateaus prematurely, obscuring ongoing physical refinement. To verify this disconnect is a dataset-agnostic pathology, we introduce ScatterPrism, an efficient generative surrogate evaluated against both the NP data and synthetic stress tests modeling challenging 1D distribution topologies. Coupling these benchmarks, we establish that physics-informed metrics continue improving long after standard loss converges. Consequently, we propose a multi-metric diagnostic protocol to ensure true kinematic fidelity without data memorization. Driven by NP challenges relevant to the forthcoming Electron-Ion Collider (EIC), this unified machinery has strong potential to extend to High-Energy Physics (HEP) applications, such as jet modeling. Furthermore, the framework holds promise for broader domains requiring rigorous generative reliability, including medical imaging, astrophysics, and quantitative finance. △ Less

Submitted 13 July, 2026; v1 submitted 1 April, 2026; originally announced April 2026.

Comments: 23 pages, 16 figures. Published in Journal of Instrumentation (AI4EIC 2025 proceedings)

Journal ref: JINST 21, C07012 (2026)

A Survey of Neural Network Variational Monte Carlo from a Computing Workload Characterization Perspective

Authors: Zhengze Xiao, Xuanzhe Ding, Yuyang Lou, Lixue Cheng, Chaojian Li

Abstract: Neural Network Variational Monte Carlo (NNVMC) has emerged as a promising paradigm for solving quantum many-body problems by combining variational Monte Carlo with expressive neural-network wave-function ansätze. Although NNVMC can achieve competitive accuracy with favorable asymptotic scaling, practical deployment remains limited by high runtime and memory cost on modern graphics processing units… ▽ More Neural Network Variational Monte Carlo (NNVMC) has emerged as a promising paradigm for solving quantum many-body problems by combining variational Monte Carlo with expressive neural-network wave-function ansätze. Although NNVMC can achieve competitive accuracy with favorable asymptotic scaling, practical deployment remains limited by high runtime and memory cost on modern graphics processing units (GPUs). Compared with language and vision workloads, NNVMC execution is shaped by physics-specific stages, including Markov-Chain Monte Carlo sampling, wave-function construction, and derivative/Laplacian evaluation, which produce heterogeneous kernel behavior and nontrivial bottlenecks. This paper provides a workload-oriented survey and empirical GPU characterization of four representative ansätze: PauliNet, FermiNet, Psiformer, and Orbformer. Using a unified profiling protocol, we analyze model-level runtime and memory trends and kernel-level behavior through family breakdown, arithmetic intensity, roofline positioning, and hardware utilization counters. The results show that end-to-end performance is often constrained by low-intensity elementwise and data-movement kernels, while the compute/memory balance varies substantially across ansätze and stages. Based on these findings, we discuss algorithm--hardware co-design implications for scalable NNVMC systems, including phase-aware scheduling, memory-centric optimization, and heterogeneous acceleration. △ Less

Submitted 22 March, 2026; v1 submitted 18 March, 2026; originally announced March 2026.

Large Language Models as Delivery Rider: Generating Instant Food Delivery Riders' Routing Decision with LLM Agent Framework

Authors: Chengbo Zhang, Zuopeng Xiao

Abstract: The utilization of Large Language Models (LLMs) to power human-like agents has shown remarkable potential in simulating individual mobility pattern. However, a significant gap remains in modeling cohorts of agents in dynamic and interactive systems where they must take strategic routing decisions to response mobility-specific task. To bridge this gap, we introduce LLM-DR, a novel agent framework d… ▽ More The utilization of Large Language Models (LLMs) to power human-like agents has shown remarkable potential in simulating individual mobility pattern. However, a significant gap remains in modeling cohorts of agents in dynamic and interactive systems where they must take strategic routing decisions to response mobility-specific task. To bridge this gap, we introduce LLM-DR, a novel agent framework designed to simulate the heterogeneous decision-making of riders in the on-demand instant delivery task scenario. Our framework is founded on two principles: 1) Empirically-grounded personas, where we use unsupervised clustering on a large-scale, real-world trajectory dataset to identify four distinct rider work strategies; and 2) Reasoning-based routing process, where each persona is instantiated as an LLM agent that employs a structured Chain-of-Thought (CoT) process to make human-like routing choices. This framework enables the construction of high-fidelity simulations to investigate how the strategic composition of a rider workforce influences system-level outcomes regarding their mobility pattern. We validate our framework on an real-world instant deliver order datasets, demonstrating its capacity to model complex rider behavior in an interactive market scenario. This work provides pioneering findings in agentic mobility system empowered by LLM. △ Less

Submitted 12 March, 2026; originally announced March 2026.

Comments: Proceedings of the 3rd International Conference on Urban Science and Intelligence

Optimization of Higher-Order Harmonic Surface Tessellations for Additively Manufactured Air-to-Air Heat Exchangers

Authors: Patrick Adegbaye, Aigbe E. Awenlimobor, Justin An, Zhang Xiao, Jiajun Xu

Abstract: Air-to-air heat exchangers are vital for energy recovery and thermal management but often suffer from reduced effectiveness, high pressure losses, and increased pumping power in conventional designs. Advances in additive manufacturing have enabled nature-inspired geometries, such as lattice and triply periodic minimal surface (TPMS) structures, which enhance heat transfer through complex first-ord… ▽ More Air-to-air heat exchangers are vital for energy recovery and thermal management but often suffer from reduced effectiveness, high pressure losses, and increased pumping power in conventional designs. Advances in additive manufacturing have enabled nature-inspired geometries, such as lattice and triply periodic minimal surface (TPMS) structures, which enhance heat transfer through complex first-order surfaces but frequently cause excessive pressure drops. This study proposes an optimized higher-order harmonic heat-transfer surface tessellation developed through an optimization framework integrating analytical and numerical methods. The goal is to improve the overall thermal-hydraulic performance of the heat exchanger over a range of operating conditions. Results of sensitivity analysis show that secondary surface modification of this type can yield significant increase in the effectiveness reaching up to 70% although with associated increase in the pressure drop. The secondary surface wave frequency was found to be a more important control parameter than the amplitude in achieving high thermal-hydraulic performance. Additionally, we show that the optimized second order harmonic-type structure achieved relatively higher effectiveness and lower pressure-drop than the gyroid structure in the turbulent flow regime for Re>=7000. Although the gyroid TPMS structure had relatively higher effectiveness in the laminar and weakly turbulent flow regime, the associated pressure drop was found to be significantly higher than that of the harmonic-type structure. △ Less

Submitted 19 February, 2026; originally announced February 2026.

Distributed physics-informed neural networks via domain decomposition for fast flow reconstruction

Authors: Yixiao Qian, Jiaxu Liu, Zewei Xia, Song Chen, Chao Xu, Shengze Cai

Abstract: Physics-Informed Neural Networks (PINNs) offer a powerful paradigm for flow reconstruction, seamlessly integrating sparse velocity measurements with the governing Navier-Stokes equations to recover complete velocity and latent pressure fields. However, scaling such models to large spatiotemporal domains is hindered by computational bottlenecks and optimization instabilities. In this work, we propo… ▽ More Physics-Informed Neural Networks (PINNs) offer a powerful paradigm for flow reconstruction, seamlessly integrating sparse velocity measurements with the governing Navier-Stokes equations to recover complete velocity and latent pressure fields. However, scaling such models to large spatiotemporal domains is hindered by computational bottlenecks and optimization instabilities. In this work, we propose a robust distributed PINNs framework designed for efficient flow reconstruction via spatiotemporal domain decomposition. A critical challenge in such distributed solvers is pressure indeterminacy, where independent sub-networks drift into inconsistent local pressure baselines. We address this issue through a reference anchor normalization strategy coupled with decoupled asymmetric weighting. By enforcing a unidirectional information flow from designated master ranks where the anchor point lies to neighboring ranks, our approach eliminates gauge freedom and guarantees global pressure uniqueness while preserving temporal continuity. Furthermore, to mitigate the Python interpreter overhead associated with computing high-order physics residuals, we implement a high-performance training pipeline accelerated by CUDA graphs and JIT compilation. Extensive validation on complex flow benchmarks demonstrates that our method achieves near-linear strong scaling and high-fidelity reconstruction, establishing a scalable and physically rigorous pathway for flow reconstruction and understanding of complex hydrodynamics. △ Less

Submitted 5 February, 2026; originally announced February 2026.

Diffuse Laser Cooling Based on the $6\mathrm{P}_{3/2}$ Excited State of Rubidium Atoms via 420 nm Blue Light

Authors: Jia Zhang, Xun Gao Zheng Xiao, Xiaolei Guan Ruihang Chen, Mengyuan Han Tiantian Shi, Jingbiao Chen

Abstract: To date, the laser cooling of rubidium atoms has inevitably relied on 780 nm cooling light corresponding to the first excited state $5\mathrm{P}_{3/2}$. Surprisingly, we demonstrate laser cooling directly utilizing 420 nm blue light for active optical clock, which corresponds to the high excited state $6\mathrm{P}_{3/2}$ of Rb atom. Experimentally, we successfully apply the 420 nm diffuse laser co… ▽ More To date, the laser cooling of rubidium atoms has inevitably relied on 780 nm cooling light corresponding to the first excited state $5\mathrm{P}_{3/2}$. Surprisingly, we demonstrate laser cooling directly utilizing 420 nm blue light for active optical clock, which corresponds to the high excited state $6\mathrm{P}_{3/2}$ of Rb atom. Experimentally, we successfully apply the 420 nm diffuse laser cooling technique to prepare a cold $^{87}\mathrm{Rb}$ atomic cloud with a length of up to one meter, and measure the cold-atom absorption spectroscopy. The cold atom number is approximately $4.4\times10^{7}$. We systematically compare the cooling effects of 420 nm and 780 nm diffuse laser cooling, and verify the feasibility of blue light cooling using high excited state. This work directly employs blue light to cool and manipulate ground-state Rb atoms to the 6P excited state, providing a new and efficient approach for the cold-atom active optical clock. It is also expected to open up research directions and application prospects in frontier fields such as Rydberg atoms, ultracold quantum gases Bose-Einstein condensation, quantum information, and so on. △ Less

Submitted 20 November, 2025; originally announced November 2025.

Comments: 11 pages, 7 figures

Overshoot-resolved transition modeling based on field inversion and symbolic regression

Authors: Lei Wu, Zuoli Xiao

Abstract: Overshoot of high-speed transitional skin-friction and heat-transfer values over their fully turbulent levels is well documented by numerous direct numerical simulations (DNS) and experimental studies. However, this high-speed-specific overshoot phenomenon remains a longstanding challenge in Reynolds-averaged Navier-Stokes (RANS) transition models. In this paper, field inversion and symbolic regre… ▽ More Overshoot of high-speed transitional skin-friction and heat-transfer values over their fully turbulent levels is well documented by numerous direct numerical simulations (DNS) and experimental studies. However, this high-speed-specific overshoot phenomenon remains a longstanding challenge in Reynolds-averaged Navier-Stokes (RANS) transition models. In this paper, field inversion and symbolic regression (FISR) methodologies are adopted to explore a generalizable and interpretable augmentation for resolving the missing overshoot characteristic. Specifically, field inversion is implemented on our previous high-speed-improved $k$-$ω$-$γ$-$\widetilde{Re}_{θ\rm{t}}$ transition-turbulence model. Then symbolic regression is employed to derive an analytical map from RANS mean flow variables to the pre-defined and inferred corrective field $β(\mathbf{x})$. Results manifest that the excavated expression faithfully reproduces the overshoot phenomena of transition region over various test cases while does not corrupt model behavior in transition location and length. Based on its transparent functional form, mechanistic investigations are conducted to illustrate the underlying logic for accurate capture of overshoot phenomenon. In addition, importance of protect function in $β(\mathbf{x})$, feasibility of a more concise expression for $β(\mathbf{x})$, and reliable performance of $β(\mathbf{x})$ in low-speed transitional flows are emphasized. △ Less

Submitted 28 October, 2025; originally announced October 2025.

Boundary layer transition induced by surface roughness distributed over a low-pressure turbine blade

Authors: Xianwen Zhu, Yuchen Ge, Yaomin Zhao, Zuoli Xiao, Richard D. Sandberg

Abstract: Direct numerical simulations of a low-pressure turbine with roughness elements distributed over the blade surface have been performed. A series of fifteen cases with varying roughness heights and streamwise wavenumbers are introduced to present a systematic study of the effect of roughness on the various transition phenomena in the suction-side boundary layer. For cases with large roughness height… ▽ More Direct numerical simulations of a low-pressure turbine with roughness elements distributed over the blade surface have been performed. A series of fifteen cases with varying roughness heights and streamwise wavenumbers are introduced to present a systematic study of the effect of roughness on the various transition phenomena in the suction-side boundary layer. For cases with large roughness heights, the boundary layer is violently disturbed by the wake of rough elements in the leading edge (LE) region, and maintains the turbulent state over the whole blade suction-side. For cases with small roughness heights, however, the disturbances induced by the LE roughness are suppressed by the favourable pressure gradient in the downstream boundary layer, and the relaminarized flow does not undergo transition until the separation near the blade trailing edge (TE). Furthermore, the streamwise wavenumber of the distributed roughness plays an important role in cases with intermediate roughness height. Specifically, cases with larger streamwise slope show earlier transition induced by strong shear layer instability, which manages to suppress the mean flow separation near the TE region. Overall, the combined effect of several factors, including the geometric effect at the blade LE and TE, the complex pressure gradient distribution across the turbine vane, and the various roughness configurations, is responsible for the intriguing boundary layer behaviours in the present study. △ Less

Submitted 22 January, 2026; v1 submitted 25 October, 2025; originally announced October 2025.

Dispersion engineered AlGaAs-on-insulator nanophotonics by distributed feedback

Authors: Francesco Rinaldo Talenti, Luca Lovisolo, Zijun Xiao, Zeina Saleh, Andrea Gerini, Carlos Alonso-Ramos, Martina Morassi, Aristide Lemaître, Stefan Wabnitz, Alfredo De Rossi, Giuseppe Leo, Laurent Vivien

Abstract: Technological advances in the fabrication of nanophotonic circuits have driven the scientific community to increasingly focus on the precise tailoring of their key optical properties, over a broadband spectral domain. In this context, the modulation of the local refractive index can be exploited to customize an effective reflectivity by the use of distributed Bragg mirrors, enabling the on-chip in… ▽ More Technological advances in the fabrication of nanophotonic circuits have driven the scientific community to increasingly focus on the precise tailoring of their key optical properties, over a broadband spectral domain. In this context, the modulation of the local refractive index can be exploited to customize an effective reflectivity by the use of distributed Bragg mirrors, enabling the on-chip integration of Fabry-Pérot resonators. The resulting cavity length is strongly wavelength-dependent, offering practical solutions to the growing demand of dispersion engineering. Owing to their typically high core-to-cladding refractive index contrast and exceptional nonlinear properties, III-V semiconductor-based platforms represent promising candidates for the fabrication of Bragg reflectors. In this work, we propose an AlGaAs-on-insulator linear resonator based on distributed Bragg mirrors. We discuss the first experimental demonstration of a systematic, shape-constrained inverse design technique which tailors a prescribed dispersion profile, showing a strong agreement between simulations and measurements. In perspective, the proposed approach offers an efficient and general response to the challenge of dispersion engineering in integrated optical circuits. △ Less

Submitted 16 October, 2025; originally announced October 2025.

Zephyrus: An Agentic Framework for Weather Science

Authors: Sumanth Varambally, Marshall Fisher, Jas Thakker, Yiwei Chen, Zhirui Xia, Yasaman Jafari, Ruijia Niu, Manas Jain, Veeramakali Vignesh Manivannan, Zachary Novack, Luyu Han, Srikar Eranky, Salva Rühling Cachay, Taylor Berg-Kirkpatrick, Duncan Watson-Parris, Yi-An Ma, Rose Yu

Abstract: Foundation models for weather science are pre-trained on vast amounts of structured numerical data and outperform traditional weather forecasting systems. However, these models lack language-based reasoning capabilities, limiting their utility in interactive scientific workflows. Large language models (LLMs) excel at understanding and generating text but cannot reason about high-dimensional meteor… ▽ More Foundation models for weather science are pre-trained on vast amounts of structured numerical data and outperform traditional weather forecasting systems. However, these models lack language-based reasoning capabilities, limiting their utility in interactive scientific workflows. Large language models (LLMs) excel at understanding and generating text but cannot reason about high-dimensional meteorological datasets. We bridge this gap by building the first agentic framework for weather science. Our framework includes a Python code-based environment for agents (ZephyrusWorld) to interact with weather data, featuring tools including a WeatherBench 2 dataset indexer, geolocator for geocoding from natural language, weather forecasting, climate simulation capabilities, and a climatology module for querying precomputed climatological statistics (e.g., means, extremes, and quantiles) across multiple timescales. We design Zephyrus, a multi-turn LLM-based weather agent that iteratively analyzes weather datasets, observes results, and refines its approach through conversational feedback loops. We accompany the agent with a new benchmark, ZephyrusBench, with a scalable data generation pipeline that constructs diverse question-answer pairs across weather-related tasks, from basic lookups to advanced forecasting, extreme event detection, and counterfactual reasoning. Experiments on this benchmark demonstrate the strong performance of Zephyrus agents over text-only baselines, outperforming them by up to 44 percentage points in correctness. However, the hard tasks are still difficult even with frontier LLMs, highlighting the challenging nature of our benchmark and suggesting room for future development. Our codebase and benchmark are available at https://github.com/Rose-STL-Lab/Zephyrus. △ Less

Submitted 16 March, 2026; v1 submitted 4 October, 2025; originally announced October 2025.

Multi-needle Localization for Pelvic Seed Implant Brachytherapy based on Tip-handle Detection and Matching

Authors: Zhuo Xiao, Fugen Zhou, Jingjing Wang, Chongyu He, Bo Liu, Haitao Sun, Zhe Ji, Yuliang Jiang, Junjie Wang, Qiuwen Wu

Abstract: Accurate multi-needle localization in intraoperative CT images is crucial for optimizing seed placement in pelvic seed implant brachytherapy. However, this task is challenging due to poor image contrast and needle adhesion. This paper presents a novel approach that reframes needle localization as a tip-handle detection and matching problem to overcome these difficulties. An anchor-free network, ba… ▽ More Accurate multi-needle localization in intraoperative CT images is crucial for optimizing seed placement in pelvic seed implant brachytherapy. However, this task is challenging due to poor image contrast and needle adhesion. This paper presents a novel approach that reframes needle localization as a tip-handle detection and matching problem to overcome these difficulties. An anchor-free network, based on HRNet, is proposed to extract multi-scale features and accurately detect needle tips and handles by predicting their centers and orientations using decoupled branches for heatmap regression and polar angle prediction. To associate detected tips and handles into individual needles, a greedy matching and merging (GMM) method designed to solve the unbalanced assignment problem with constraints (UAP-C) is presented. The GMM method iteratively selects the most probable tip-handle pairs and merges them based on a distance metric to reconstruct 3D needle paths. Evaluated on a dataset of 100 patients, the proposed method demonstrates superior performance, achieving higher precision and F1 score compared to a segmentation-based method utilizing the nnUNet model,thereby offering a more robust and accurate solution for needle localization in complex clinical scenarios. △ Less

Submitted 19 May, 2026; v1 submitted 22 September, 2025; originally announced September 2025.

An Iterative LLM Framework for SIBT utilizing RAG-based Adaptive Weight Optimization

Authors: Zhuo Xiao, Qinglong Yao, Jingjing Wang, Fugen Zhou, Bo Liu, Haitao Sun, Zhe Ji, Yuliang Jiang, Junjie Wang, Qiuwen Wu

Abstract: Seed implant brachytherapy (SIBT) is an effective cancer treatment modality; however, clinical planning often relies on manual adjustment of objective function weights, leading to inefficiencies and suboptimal results. This study proposes an adaptive weight optimization framework for SIBT planning, driven by large language models (LLMs). A locally deployed DeepSeek-R1 LLM is integrated with an aut… ▽ More Seed implant brachytherapy (SIBT) is an effective cancer treatment modality; however, clinical planning often relies on manual adjustment of objective function weights, leading to inefficiencies and suboptimal results. This study proposes an adaptive weight optimization framework for SIBT planning, driven by large language models (LLMs). A locally deployed DeepSeek-R1 LLM is integrated with an automatic planning algorithm in an iterative loop. Starting with fixed weights, the LLM evaluates plan quality and recommends new weights in the next iteration. This process continues until convergence criteria are met, after which the LLM conducts a comprehensive evaluation to identify the optimal plan. A clinical knowledge base, constructed and queried via retrieval-augmented generation (RAG), enhances the model's domain-specific reasoning. The proposed method was validated on 23 patient cases, showing that the LLM-assisted approach produces plans that are comparable to or exceeding clinically approved and fixed-weight plans, in terms of dose homogeneity for the clinical target volume (CTV) and sparing of organs at risk (OARs). The study demonstrates the potential use of LLMs in SIBT planning automation. △ Less

Submitted 10 September, 2025; originally announced September 2025.

Modeling and simulation of inductionless magnetohydrodynamic free surface problems with unmatched densities

Authors: Jiancheng Wang, Maojun Li, Zeyu Xia, Liwei Xu

Abstract: We propose a new diffuse interface model for simulating an inductionless magnetohydrodynamic (MHD) free surface problem. By using the Onsager's variational principle and the laws of thermodynamics, we derive a thermodynamically consistent system that couples the Cahn--Hilliard equation modeling phase separation, the Navier--Stokes equations governing fluid motion, and a generalized Darcy's law acc… ▽ More We propose a new diffuse interface model for simulating an inductionless magnetohydrodynamic (MHD) free surface problem. By using the Onsager's variational principle and the laws of thermodynamics, we derive a thermodynamically consistent system that couples the Cahn--Hilliard equation modeling phase separation, the Navier--Stokes equations governing fluid motion, and a generalized Darcy's law accounting for electromagnetic effects. In contrast to existing diffuse interface MHD models, the proposed model can handle general material properties in practical engineering applications. Furthermore, through asymptotic arguments, we investigate the sharp interface limit, and then demonstrate that the classical sharp interface model can be recovered as the interface thickness approaches zero, theoretically validating the proposed diffuse interface model as an approximate approach. An efficient decoupled, linear, and charge-conservative finite element scheme is designed, and it significantly facilitates the large-scale and accurate numerical simulations involving large parameter ratios. Finally, we present several three-dimensional numerical experiments of magnetic damping effects on bubble dynamics for the demonstration of the capability of the proposed model and method in capturing complex MHD phenomena. △ Less

Submitted 26 September, 2025; v1 submitted 19 July, 2025; originally announced July 2025.

Deciphering Delivery Mobility: A City-Scale, Path-Reconstructed Trajectory Dataset of Instant Delivery Riders

Authors: Chengbo Zhang, Yonglin Li, Zuopeng Xiao

Abstract: The rapid expansion of the on-demand economy has profoundly reshaped urban mobility and logistics, yet high-resolution trajectory data on delivery riders' consistent movements remains scarce. Here, we present a city-scale, high-resolution spatiotemporal trajectory dataset of on-demand instant delivery riders in Beijing. This dataset was produced through a path-reconstruction methodology applied to… ▽ More The rapid expansion of the on-demand economy has profoundly reshaped urban mobility and logistics, yet high-resolution trajectory data on delivery riders' consistent movements remains scarce. Here, we present a city-scale, high-resolution spatiotemporal trajectory dataset of on-demand instant delivery riders in Beijing. This dataset was produced through a path-reconstruction methodology applied to an open dataset containing delivery order information. Subsequently, detailed and continuous trajectories were reconstructed by simulating cycling routes via a major online map service to ensure they were realistically aligned. For validation, the reconstructed paths were compared against ground-truth travel metrics, revealing a strong correlation with actual travel patterns. The analysis yielded Pearson correlation coefficients of 0.92 for route distance and 0.79 for route duration. This high fidelity ensures the dataset's utility for describing delivery riders' mobility. This publicly available resource offers unprecedented opportunities for researchers in urban planning, transportation studies, logistics optimization, and computational social science to investigate rider behavior, model urban freight systems, and develop more efficient and sustainable city-wide logistics solutions. △ Less

Submitted 15 July, 2025; originally announced July 2025.

Comments: Data descriptor

NNQS-AFQMC: Neural network quantum states enhanced fermionic quantum Monte Carlo

Authors: Zhi-Yu Xiao, Bowen Kan, Huan Ma, Bowen Zhao, Honghui Shang

Abstract: We introduce an efficient approach to implement neural network quantum states (NNQS) as trial wavefunctions in auxiliary-field quantum Monte Carlo (AFQMC). NNQS are a recently developed class of variational ansätze capable of flexibly representing many-body wavefunctions, though they often incur a high computational cost during optimization. AFQMC, on the other hand, is a powerful stochastic proje… ▽ More We introduce an efficient approach to implement neural network quantum states (NNQS) as trial wavefunctions in auxiliary-field quantum Monte Carlo (AFQMC). NNQS are a recently developed class of variational ansätze capable of flexibly representing many-body wavefunctions, though they often incur a high computational cost during optimization. AFQMC, on the other hand, is a powerful stochastic projector approach for ground-state calculations, but it normally requires an approximate constraint via a trial wavefunction or trial density matrix, whose quality affects the accuracy. Recently it has been shown (Xiao et al, arXiv2505.18519) that a broad class of highly correlated wave-functions can be integrated into AFQMC through stochastic sampling techniques. In this work, we apply this approach and present a direct integration of NNQS with AFQMC, allowing NNQS to serve as high-quality trial wavefunctions for AFQMC with manageable computational cost. We test the NNQS-AFQMC method on the challenging nitrogen molecule (N$_2$) at stretched geometries. Our results demonstrate that AFQMC with an NNQS trial wavefunction can attain near-exact total energies, highlighting the potential of AFQMC with NNQS to overcome longstanding challenges in strongly correlated electronic structure calculations. We also outline future research directions for improving this promising methodology. △ Less

Submitted 3 October, 2025; v1 submitted 10 July, 2025; originally announced July 2025.

Rubber band filters: optimal padding without edge artifacts

Authors: Zhenyang Xiao, David Burghoff

Abstract: Bandpass filtering techniques are widely used in spectroscopy. However, conventional symmetric-padding filtering methods introduce boundary artifacts that distort the signal at the edges. We present a rubber band filter: a robust method for achieving band-limited filtering without these detrimental edge artifacts. The technique applies an optimal padding scheme during the filtering process, thereb… ▽ More Bandpass filtering techniques are widely used in spectroscopy. However, conventional symmetric-padding filtering methods introduce boundary artifacts that distort the signal at the edges. We present a rubber band filter: a robust method for achieving band-limited filtering without these detrimental edge artifacts. The technique applies an optimal padding scheme during the filtering process, thereby overcoming longstanding challenges in achieving artifact-free filtering. Importantly, it is iterative and requires only a few extra Fourier transforms over conventional approaches. We demonstrate its superiority and versatility by applying it to three spectroscopic examples -- time-domain spectroscopy, Fourier-transform spectroscopy, and dual-comb spectroscopy. △ Less

Submitted 9 July, 2025; originally announced July 2025.

Laser Amplification in $e^{-}$-$μ^{-}$-ion Plasmas

Authors: Y. Chen, R. Ou, H. Wang, S. J. Chen, Y. X. Zhong, Y. G. Chen, S. Tan, Y. X. Li, C. Y. Zheng, Z. J. Liu, L. H. Cao, M. M. Zhang, D. P. Feng, W. J. Zuo, C. Z. Xiao

Abstract: We investigate laser amplification in $e^{-}$-$μ^{-}$-ion plasmas, where negative muons partially replace electrons. Theoretical results reveal a hybrid plasma wave, called $μ$-wave that exhibits ion-acoustic behavior in long-wavelength regime and Langmuir-like behavior in short-wavelength regime. Besides, the Landau damping of $μ$-wave is smaller than that of Langmuir wave. Particle-in-cell (PIC)… ▽ More We investigate laser amplification in $e^{-}$-$μ^{-}$-ion plasmas, where negative muons partially replace electrons. Theoretical results reveal a hybrid plasma wave, called $μ$-wave that exhibits ion-acoustic behavior in long-wavelength regime and Langmuir-like behavior in short-wavelength regime. Besides, the Landau damping of $μ$-wave is smaller than that of Langmuir wave. Particle-in-cell (PIC) simulations confirm the theoretical results of instabilities in$e^{-}$-$μ^{-}$-ion plasmas. The $μ$-wave enables efficient laser amplification by suppressing pump-driven spontaneous instabilities through enhanced Landau damping of Langmuir waves. Compared to Raman amplification, $μ$-wave amplification can maintain the Gaussian waveform of the seed laser, avoiding pulse splitting. Compared to strongcoupling Brillouin amplification, $μ$-wave amplification exhibits weaker filamentation instability. Our theoretical model can be generalized to other plasma systems containing two species of negatively charged particles, such as two-temperature electron plasmas and negative-ion plasma. These findings establish $e^{-}$-$μ^{-}$-ion plasma as a promising medium for advanced laser amplification schemes. △ Less

Submitted 6 October, 2025; v1 submitted 6 July, 2025; originally announced July 2025.

Comments: 9 pages, 6 figures

Simulation studies of the isovector reorientation effect of deuteron scattering on heavy target

Authors: Baiting Tian, Boyuan Zhang, Dawei Si, Sheng Xiao, Yijie Wang, Tadaaki Isobe, Hideaki Otsu, Li Ou, Zhigang Xiao

Abstract: The isovector reorientation (IVR) effect of deuteron scattering on heavy target provides a novel means to probe the nuclear isovector potential, which gives rise to the nuclear symmetry energy. The simulation studies on the experimental measurement of IVR effect using the SAMURAI terminal at RIKEN Nishina center have been performed to demonstrate the feasibility of the experiment. By introducing a… ▽ More The isovector reorientation (IVR) effect of deuteron scattering on heavy target provides a novel means to probe the nuclear isovector potential, which gives rise to the nuclear symmetry energy. The simulation studies on the experimental measurement of IVR effect using the SAMURAI terminal at RIKEN Nishina center have been performed to demonstrate the feasibility of the experiment. By introducing a well-designed polarimeter to detect the $\mathrm{p}(\vec{\mathrm{d}}, \mathrm{d})\mathrm{p}$ elastic scattering, monitoring of the tensor polarization of the deuteron beam can be implemented. The protons and neutrons produced by the breakup of polarized deuterons scattering off heavy targets are designed to be measured by proton drift chamber (PDC) combined with the SAMURAI magnet and NEBULA detector, respectively. The detector responses are simulated using Geant4 framework, where the events of the deuteron elastic breakup are generated by an Improved Quantum Molecular Dynamics model. The results of reconstructing the deuteron breakup events demonstrate the feasibility of detecting the IVR effect at SAMURAI with both longitudinal and transverse tensor polarized deuteron beams with a polarization degree of approximately 80\%. △ Less

Submitted 22 October, 2025; v1 submitted 17 June, 2025; originally announced June 2025.

Cascaded quantum time transfer breaking the no-cloning barrier with entanglement relay architecture

Authors: H. Hong, X. Xiang, R. Quan, B. Shi, Y. Liu, Z. Xia, T. Liu, X. Li, M. Cao, S. Zhang, K. Guo, R. Dong

Abstract: Quantum two-way time transfer (Q-TWTT) leveraging energy-time entangled biphotons has achieved sub-picosecond stability but faces fundamental distance limitations due to the no-cloning theorem's restriction on quantum amplification. To overcome this challenge, we propose a cascaded Q-TWTT architecture employing relay stations that generate and distribute new energy-time entangled biphotons after e… ▽ More Quantum two-way time transfer (Q-TWTT) leveraging energy-time entangled biphotons has achieved sub-picosecond stability but faces fundamental distance limitations due to the no-cloning theorem's restriction on quantum amplification. To overcome this challenge, we propose a cascaded Q-TWTT architecture employing relay stations that generate and distribute new energy-time entangled biphotons after each transmission segment. Theoretical modeling reveals sublinear standard deviation growth (merely N increase for N equidistant segments), enabling preservation of sub-picosecond stability over extended distances. We experimentally validate this approach using a three-station cascaded configuration over 200 km fiber segments, demonstrating strong agreement with theory. Utilizing independent Rb clocks at end and relay stations with online frequency skew correction, we achieve time stabilities of 3.82 ps at 10 s and 0.39 ps at 5120 s. The consistency in long-term stability between cascaded and single-segment configurations confirms high-precision preservation across modular quantum networks. This work establishes a framework for long-distance quantum time transfer that surpasses the no-cloning barrier, providing a foundation for future quantum-network timing infrastructure. △ Less

Submitted 15 June, 2025; originally announced June 2025.

Liquid combs: broadband light with equidistance and without stability

Authors: Mithun Roy, Tianyi Zeng, Zhenyang Xiao, Chao Dong, Sadhvikas Addamane, Qing Hu, David Burghoff

Abstract: Broadband light sources with well-defined spectral structures are vital for science and technology. However, the evenly spaced lines of frequency combs represent only a small subset of all possible structured white-light sources. We demonstrate liquid combs: optical states that preserve spectral equidistance but lack temporal stability. By engineering the gain and dispersion of semiconductor laser… ▽ More Broadband light sources with well-defined spectral structures are vital for science and technology. However, the evenly spaced lines of frequency combs represent only a small subset of all possible structured white-light sources. We demonstrate liquid combs: optical states that preserve spectral equidistance but lack temporal stability. By engineering the gain and dispersion of semiconductor laser cavities, we produce light that possesses rapid phase fluctuations but maintains relative phase differences between modes that vary identically. We show experimentally that this phenomenon occurs in multiple laser platforms -- across multiple octaves -- through the creation of a metrological technique that determines the phase differences. We also show theoretically that this is a general phenomenon that can be described using a mean-field theory. These liquid combs are attractive for many applications due to having wider bandwidths than frequency combs, and more generally, they represent the long-sought realization of structured white-light sources that are not combs. △ Less

Submitted 19 May, 2025; originally announced May 2025.

Supporting renewable energy planning and operation with data-driven high-resolution ensemble weather forecast

Authors: Jingnan Wang, Jie Chao, Shangshang Yang, Kaijun Ren, Kefeng Deng, Xi Chen, Yaxin Liu, Hanqiuzi Wen, Ziniu Xiao, Lifeng Zhang, Xiaodong Wang, Jiping Guan, Baoxiang Pan

Abstract: The planning and operation of renewable energy, especially wind power, depend crucially on accurate, timely, and high-resolution weather information. Coarse-grid global numerical weather forecasts are typically downscaled to meet these requirements, introducing challenges of scale inconsistency, process representation error, computation cost, and entanglement of distinct uncertainty sources from c… ▽ More The planning and operation of renewable energy, especially wind power, depend crucially on accurate, timely, and high-resolution weather information. Coarse-grid global numerical weather forecasts are typically downscaled to meet these requirements, introducing challenges of scale inconsistency, process representation error, computation cost, and entanglement of distinct uncertainty sources from chaoticity, model bias, and large-scale forcing. We address these challenges by learning the climatological distribution of a target wind farm using its high-resolution numerical weather simulations. An optimal combination of this learned high-resolution climatological prior with coarse-grid large scale forecasts yields highly accurate, fine-grained, full-variable, large ensemble of weather pattern forecasts. Using observed meteorological records and wind turbine power outputs as references, the proposed methodology verifies advantageously compared to existing numerical/statistical forecasting-downscaling pipelines, regarding either deterministic/probabilistic skills or economic gains. Moreover, a 100-member, 10-day forecast with spatial resolution of 1 km and output frequency of 15 min takes < 1 hour on a moderate-end GPU, as contrast to $\mathcal{O}(10^3)$ CPU hours for conventional numerical simulation. By drastically reducing computational costs while maintaining accuracy, our method paves the way for more efficient and reliable renewable energy planning and operation. △ Less

Submitted 27 June, 2025; v1 submitted 7 May, 2025; originally announced May 2025.

Computational Orthodontic Force Simulation: A Review

Authors: Waheed Ahmad, Jing Xiong, Zeyang Xia

Abstract: In orthodontic treatment, the biological response of the tooth, periodontal ligament, and bone complex to orthodontic force is crucial in influencing treatment outcomes. The challenge lies in accurately measuring, estimating, and predicting these forces during clinical procedures. This review aims to fill the gap in the literature by systematically summarizing existing research on orthodontic forc… ▽ More In orthodontic treatment, the biological response of the tooth, periodontal ligament, and bone complex to orthodontic force is crucial in influencing treatment outcomes. The challenge lies in accurately measuring, estimating, and predicting these forces during clinical procedures. This review aims to fill the gap in the literature by systematically summarizing existing research on orthodontic force simulation, examining common loading techniques and technologies, and discussing the potential for refining the orthodontic force simulation process. The literature was comprehensively reviewed, with an emphasis on the exploration of the biological mechanism of tooth movement. Studies were categorized based on force-loading techniques for both fixed and invisible orthodontic appliances. Finite element (FE) analysis stands out as the predominant technique for orthodontic force simulation, with a significant focus on fixed orthodontics but limited emphasis on invisible orthodontics. Current orthodontic force simulations tend to be fragmented, often considering only the instantaneous response to applied forces. There exists an urgent demand for a sophisticated analytical simulation model. Such a model, possibly leveraging advanced technologies like deep learning, holds the promise of forecasting orthodontic treatment outcomes with heightened precision and efficiency. △ Less

Submitted 31 March, 2025; originally announced March 2025.

Comments: 19 pages, 4 figure, 1 table

Linear Response of CsI(Tl) Crystal to Energetic Photons below 20 MeV

Authors: Junhuai Xu, Dawei Si, Yuhao Qin, Mengke Xu, Kaijie Chen, Zirui Hao, Gongtao Fan, Hongwei Wang, Yijie Wang, Zhigang Xiao

Abstract: The linear response of CsI(Tl) crystals to $γ$-rays plays a crucial role in their calibration, as any deviation from linearity can introduce systematic errors not negligible in the measurement of $γ$ energy spectra, particularly at high energies. In this study, the responses of CsI(Tl) crystals to high-energy photons up to 20 MeV are investigated using quasi monochromatic $γ$ beam provided by the… ▽ More The linear response of CsI(Tl) crystals to $γ$-rays plays a crucial role in their calibration, as any deviation from linearity can introduce systematic errors not negligible in the measurement of $γ$ energy spectra, particularly at high energies. In this study, the responses of CsI(Tl) crystals to high-energy photons up to 20 MeV are investigated using quasi monochromatic $γ$ beam provided by the Shanghai Laser Electron Gamma Source. The spectra are folded using a detector filter implemented by Geant4. Both quadratic and linear fits to six energy points are used to assess the linearity of the CsI(Tl) detector. The results demonstrate that the difference between the linear and non-linear fits is at the level of 4\%. Applying these findings to the $γ$ hodoscope of the Compact Spectrometer for Heavy Ion Experiment (CSHINE), the potential systematic uncertainties caused by CsI(Tl) non-linearity are evaluated. This work provides a comprehensive calibration methodology for employing CsI(Tl) crystal to detect high energy $γ$-rays. △ Less

Submitted 12 May, 2025; v1 submitted 13 March, 2025; originally announced March 2025.

Journal ref: Nuclear Instruments and Methods in Physics Research Section A, Volume 1080, November 2025, 170787

Generative assimilation and prediction for weather and climate

Authors: Shangshang Yang, Congyi Nai, Xinyan Liu, Weidong Li, Jie Chao, Jingnan Wang, Leyi Wang, Xichen Li, Xi Chen, Bo Lu, Ziniu Xiao, Niklas Boers, Huiling Yuan, Baoxiang Pan

Abstract: Machine learning models have shown great success in predicting weather up to two weeks ahead, outperforming process-based benchmarks. However, existing approaches mostly focus on the prediction task, and do not incorporate the necessary data assimilation. Moreover, these models suffer from error accumulation in long roll-outs, limiting their applicability to seasonal predictions or climate project… ▽ More Machine learning models have shown great success in predicting weather up to two weeks ahead, outperforming process-based benchmarks. However, existing approaches mostly focus on the prediction task, and do not incorporate the necessary data assimilation. Moreover, these models suffer from error accumulation in long roll-outs, limiting their applicability to seasonal predictions or climate projections. Here, we introduce Generative Assimilation and Prediction (GAP), a unified deep generative framework for assimilation and prediction of both weather and climate. By learning to quantify the probabilistic distribution of atmospheric states under observational, predictive, and external forcing constraints, GAP excels in a broad range of weather-climate related tasks, including data assimilation, seamless prediction, and climate simulation. In particular, GAP is competitive with state-of-the-art ensemble assimilation, probabilistic weather forecast and seasonal prediction, yields stable millennial simulations, and reproduces climate variability from daily to decadal time scales. △ Less

Submitted 4 March, 2025; originally announced March 2025.

Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems

Authors: Yunyang Li, Zaishuo Xia, Lin Huang, Xinran Wei, Han Yang, Sam Harshe, Zun Wang, Chang Liu, Jia Zhang, Bin Shao, Mark B. Gerstein

Abstract: Density Functional Theory (DFT) is a pivotal method within quantum chemistry and materials science, with its core involving the construction and solution of the Kohn-Sham Hamiltonian. Despite its importance, the application of DFT is frequently limited by the substantial computational resources required to construct the Kohn-Sham Hamiltonian. In response to these limitations, current research has… ▽ More Density Functional Theory (DFT) is a pivotal method within quantum chemistry and materials science, with its core involving the construction and solution of the Kohn-Sham Hamiltonian. Despite its importance, the application of DFT is frequently limited by the substantial computational resources required to construct the Kohn-Sham Hamiltonian. In response to these limitations, current research has employed deep-learning models to efficiently predict molecular and solid Hamiltonians, with roto-translational symmetries encoded in their neural networks. However, the scalability of prior models may be problematic when applied to large molecules, resulting in non-physical predictions of ground-state properties. In this study, we generate a substantially larger training set (PubChemQH) than used previously and use it to create a scalable model for DFT calculations with physical accuracy. For our model, we introduce a loss function derived from physical principles, which we call Wavefunction Alignment Loss (WALoss). WALoss involves performing a basis change on the predicted Hamiltonian to align it with the observed one; thus, the resulting differences can serve as a surrogate for orbital energy differences, allowing models to make better predictions for molecular orbitals and total energies than previously possible. WALoss also substantially accelerates self-consistent-field (SCF) DFT calculations. Here, we show it achieves a reduction in total energy prediction error by a factor of 1347 and an SCF calculation speed-up by a factor of 18%. These substantial improvements set new benchmarks for achieving accurate and applicable predictions in larger molecular systems. △ Less

Submitted 20 March, 2025; v1 submitted 26 February, 2025; originally announced February 2025.

PyTorchFire: A GPU-Accelerated Wildfire Simulator with Differentiable Cellular Automata

Authors: Zeyu Xia, Sibo Cheng

Abstract: Accurate and rapid prediction of wildfire trends is crucial for effective management and mitigation. However, the stochastic nature of fire propagation poses significant challenges in developing reliable simulators. In this paper, we introduce PyTorchFire, an open-access, PyTorch-based software that leverages GPU acceleration. With our redesigned differentiable wildfire Cellular Automata (CA) mode… ▽ More Accurate and rapid prediction of wildfire trends is crucial for effective management and mitigation. However, the stochastic nature of fire propagation poses significant challenges in developing reliable simulators. In this paper, we introduce PyTorchFire, an open-access, PyTorch-based software that leverages GPU acceleration. With our redesigned differentiable wildfire Cellular Automata (CA) model, we achieve millisecond-level computational efficiency, significantly outperforming traditional CPU-based wildfire simulators on real-world-scale fires at high resolution. Real-time parameter calibration is made possible through gradient descent on our model, aligning simulations closely with observed wildfire behavior both temporally and spatially, thereby enhancing the realism of the simulations. Our PyTorchFire simulator, combined with real-world environmental data, demonstrates superior generalizability compared to supervised learning surrogate models. Its ability to predict and calibrate wildfire behavior in real-time ensures accuracy, stability, and efficiency. PyTorchFire has the potential to revolutionize wildfire simulation, serving as a powerful tool for wildfire prediction and management. △ Less

Submitted 20 June, 2026; v1 submitted 25 February, 2025; originally announced February 2025.

Comments: 19 pages, 14 figures. Published in Environmental Modelling & Software

Journal ref: Environ. Model. Softw. 188, 106401 (2025)

Room-temperature field-tunable radiofrequency rectification in epitaxial SrIrO3 films

Authors: Liang Zhou, Zongzheng Du, Jinhua Wang, Pingbo Chen, Bicong Ye, Tao Feng, Jiahao Yang, Zehao Xiao, Meng Yang, Junxue Li, Wenqing Zhang, Hai-zhou Lu, Hongtao He

Abstract: Although significant advancements have been made in wireless technologies and portable devices, it remains a challenge for high-frequency and nanowatt-level radiofrequency rectification. In this work, we report a pronounced radiofrequency rectification up to 37 GHz in nominally centrosymmetric SrIrO3 epitaxial films, with the minimum detectable power as low as ~300 nanowatts. Strikingly, the SrIrO… ▽ More Although significant advancements have been made in wireless technologies and portable devices, it remains a challenge for high-frequency and nanowatt-level radiofrequency rectification. In this work, we report a pronounced radiofrequency rectification up to 37 GHz in nominally centrosymmetric SrIrO3 epitaxial films, with the minimum detectable power as low as ~300 nanowatts. Strikingly, the SrIrO3 rectifier is highly field-tunable and exhibits a strong in-plane field anisotropy, thus showing a unique advantage in broad-band radiofrequency rectification. The rectification effect can persist up to at least 360 K and shows a sensitive temperature dependence including a sign inversion. By a systematic study of the nonlinear transport properties of SrIrO3, it is further revealed that the radiofrequency rectification originates from the nonlinear Hall effect with the dominant contribution from field-induced Berry curvature dipole. Our work demonstrates the superior performance of the field-tunable SrIrO3 rectifiers, unleashing the great application potential of centrosymmetric materials in harvesting and detecting ambient electromagnetic energy. △ Less

Submitted 23 February, 2025; originally announced February 2025.

Comments: 4 figures

High-Order Exceptional Point-Based Rotation Sensing in Anti-Parity time Symmetric Microresonators

Authors: Wenxiu Li, Zelei Li, Jincheng Li, Xin Sun, Zongqi Yang, Ce Qin, Xinyao Huang, Anping Huang, Hao Zhang, Zhisong Xiao

Abstract: Exceptional points (EPs), which arise from non-Hermitian systems, have been extensively investigated for the development of high-performance gyroscopes. However, operating a non-Hermitian gyroscope at high-order EP (HOEP) to achieve extreme performance requires strict and precise control of parameters. Here, we propose the design of an anti-parity-time (anti-PT) symmetric optical gyroscope operati… ▽ More Exceptional points (EPs), which arise from non-Hermitian systems, have been extensively investigated for the development of high-performance gyroscopes. However, operating a non-Hermitian gyroscope at high-order EP (HOEP) to achieve extreme performance requires strict and precise control of parameters. Here, we propose the design of an anti-parity-time (anti-PT) symmetric optical gyroscope operating at a fourth-order EP, achieving both ultra-sensitivity and high robustness. Our configuration exhibits eigenfrequency splitting two orders of magnitude higher than that of anti-PT gyroscopes operating at second-order EP. Furthermore, we demonstrate a significant reduction in angular random walk (ARW) under noise limits, compared to anti-parity symmetric gyroscopes based on second-order EP. Our results provide a novel approach for developing high-sensitivity rotation detection based on HOEPs. △ Less

Submitted 7 February, 2025; originally announced February 2025.

Stacking effects on magnetic, vibrational, and optical properties of CrSBr bilayers

Authors: Huicong Li, Yali Yang, Zhonghao Xia, Yateng Wang, Jiacheng Wei, Jiangang He, Rongming Wang

Abstract: The van der Waals layered semiconductor CrSBr, which exhibits A-type antiferromagnetism and a relatively high Néel temperature, has been successfully exfoliated into atomically thin sheets. In this study, we investigate the structural, lattice dynamical, electronic, magnetic, and optical properties of four distinct stacking structures of CrSBr bilayers using first-principles calculations and Monte… ▽ More The van der Waals layered semiconductor CrSBr, which exhibits A-type antiferromagnetism and a relatively high Néel temperature, has been successfully exfoliated into atomically thin sheets. In this study, we investigate the structural, lattice dynamical, electronic, magnetic, and optical properties of four distinct stacking structures of CrSBr bilayers using first-principles calculations and Monte Carlo simulations. Our findings show that though the most energetically favorable bilayer structure retains the stacking pattern of the bulk counterpart, three other high-symmetry stacking structures can be achieved by sliding one of the layers along three distinct directions, with energy costs comparable to that observed in MoS$_2$ bilayer. All these four bilayers exhibit semiconductor behavior with A-type antiferromagnetic ordering, similar to the bulk material, and demonstrate closely aligned Néel temperatures. Moreover, these bilayers exhibit relatively low lattice thermal conductivities, pronounced anisotropy, and a strong dependence on stacking patterns. This behavior is attributed to significant phonon-phonon scattering arising from avoided crossings between acoustic and optical phonons, as well as the presence of flat optical phonon bands in the low-frequency region. While the electronic structures and optical properties of these bilayers show weak dependence on the stacking pattern for antiferromagnetic ordering, they undergo significant changes for ferromagnetic ordering, influencing the band gap, valence and conduction band splitting, and effective mass. Furthermore, we found that antiferromagnetic ordering can transition to ferromagnetic under intense visible light illumination. Thus, the integration of layer stacking and visible light illumination offers an effective means to control the heat transfer, magnetic, and optical properties of CrSBr bilayers. △ Less

Submitted 5 February, 2025; originally announced February 2025.

Comments: 15 pages, 9 figures

Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity

Authors: Erpai Luo, Xinran Wei, Lin Huang, Yunyang Li, Han Yang, Zaishuo Xia, Zun Wang, Chang Liu, Bin Shao, Jia Zhang

Abstract: Hamiltonian matrix prediction is pivotal in computational chemistry, serving as the foundation for determining a wide range of molecular properties. While SE(3) equivariant graph neural networks have achieved remarkable success in this domain, their substantial computational cost--driven by high-order tensor product (TP) operations--restricts their scalability to large molecular systems with exten… ▽ More Hamiltonian matrix prediction is pivotal in computational chemistry, serving as the foundation for determining a wide range of molecular properties. While SE(3) equivariant graph neural networks have achieved remarkable success in this domain, their substantial computational cost--driven by high-order tensor product (TP) operations--restricts their scalability to large molecular systems with extensive basis sets. To address this challenge, we introduce SPHNet, an efficient and scalable equivariant network, that incorporates adaptive SParsity into Hamiltonian prediction. SPHNet employs two innovative sparse gates to selectively constrain non-critical interaction combinations, significantly reducing tensor product computations while maintaining accuracy. To optimize the sparse representation, we develop a Three-phase Sparsity Scheduler, ensuring stable convergence and achieving high performance at sparsity rates of up to 70%. Extensive evaluations on QH9 and PubchemQH datasets demonstrate that SPHNet achieves state-of-the-art accuracy while providing up to a 7x speedup over existing models. Beyond Hamiltonian prediction, the proposed sparsification techniques also hold significant potential for improving the efficiency and scalability of other SE(3) equivariant networks, further broadening their applicability and impact. Our code can be found at https://github.com/microsoft/SPHNet. △ Less

Submitted 22 May, 2025; v1 submitted 3 February, 2025; originally announced February 2025.

Velocity-comb modulation transfer spectroscopy

Authors: Xiaolei Guan, Zheng Xiao, Zijie Liu, Zhiyang Wang, Jia Zhang, Xun Gao, Pengyuan Chang, Tiantian Shi, Jingbiao Chen

Abstract: Sub-Doppler laser spectroscopy is a crucial technique for laser frequency stabilization, playing a significant role in atomic physics, precision measurement, and quantum communication. However, recent efforts to improve frequency stability appear to have reached a bottleneck, as they primarily focus on external technical approaches while neglecting the fundamental issue of low atomic utilization (… ▽ More Sub-Doppler laser spectroscopy is a crucial technique for laser frequency stabilization, playing a significant role in atomic physics, precision measurement, and quantum communication. However, recent efforts to improve frequency stability appear to have reached a bottleneck, as they primarily focus on external technical approaches while neglecting the fundamental issue of low atomic utilization (< 1%), caused by only near-zero transverse velocity atoms involved in the transition. Here, we propose a velocity-comb modulation transfer spectroscopy (MTS) solution that takes advantage of the velocity-selective resonance effect of multi-frequency comb lasers to enhance the utilization of non-zero-velocity atoms. In the probe-pump configuration, each pair of counter-propagating lasers interacts with atoms from different transverse velocity-comb groups, independently contributing to the spectral amplitude and signal-to-noise ratio. Preliminary proof-of-principle results show that the frequency stability of the triple-frequency laser is optimized by nearly a factor of \sqrt{3} compared to the single-frequency laser, consistent with theoretical expectations. With more frequency comb components, MTS-stabilized lasers are expected to achieve order-of-magnitude breakthroughs in frequency stability, taking an important step toward next-generation compact optical clocks. This unique method can also be widely applied to any quantum system with a wide velocity distribution, inspiring innovative advances in numerous fields with a fresh perspective. △ Less

Submitted 27 January, 2025; originally announced January 2025.

Comments: 8 pages, 5 figures

Modulation of nanowire emitter arrays using micro-LED technology

Authors: Zhongyi Xia, Dimitars Jevtics, Benoit Guilhabert, Jonathan J. D. McKendry, Qian Gao, Hark Hoe Tan, Chennupati Jagadish, Martin D. Dawson, Michael J. Strain

Abstract: A scalable excitation platform for nanophotonic emitters using individually addressable micro-LED-on-CMOS arrays is demonstrated for the first time. Heterogeneous integration by transfer-printing of semiconductor nanowires was used for the deterministic assembly of the infrared emitters embedded in polymer optical waveguides with high yield and positional accuracy. Direct optical pumping of these… ▽ More A scalable excitation platform for nanophotonic emitters using individually addressable micro-LED-on-CMOS arrays is demonstrated for the first time. Heterogeneous integration by transfer-printing of semiconductor nanowires was used for the deterministic assembly of the infrared emitters embedded in polymer optical waveguides with high yield and positional accuracy. Direct optical pumping of these emitters is demonstrated using micro-LED pixels as source, with optical modulation (on-off keying) measured up to 150 MHz. A micro-LED-on-CMOS array of pump sources were employed to demonstrate individual control of multiple waveguide coupled nanowire emitters in parallel, paving the way for future large scale photonic integrated circuit applications. △ Less

Submitted 9 January, 2025; originally announced January 2025.

Observational Properties of Harmonic EMIC waves: Statistical Study

Authors: Shujie Gu, Xu Liu, Lunjin Chen, Maria Usanova, Zhiyang Xia, Wenyao Gu

Abstract: Electromagnetic ion cyclotron (EMIC) waves are discrete electromagnetic emissions separated by multiple ion gyrofrequencies. Harmonic EMIC waves are defined as waves with a strong electric or magnetic field (or both) at the harmonics of the fundamental EMIC mode. In this paper, for the first time, we present a statistical study on harmonic EMIC waves by the Van Allen Probes. The EMIC waves are cat… ▽ More Electromagnetic ion cyclotron (EMIC) waves are discrete electromagnetic emissions separated by multiple ion gyrofrequencies. Harmonic EMIC waves are defined as waves with a strong electric or magnetic field (or both) at the harmonics of the fundamental EMIC mode. In this paper, for the first time, we present a statistical study on harmonic EMIC waves by the Van Allen Probes. The EMIC waves are categorized into three types based on their harmonics: (1) fundamental mode only (without higher harmonics), (2) electrostatic (ES) harmonics, and (3) electromagnetic (EM) harmonics. Our statistical study shows that ES and EM harmonic EMIC waves predominantly occur on the dayside, outside the plasmasphere with $L >5$ and are associated with a low $f_{pe}/f_{ce}$, a high proton $β_H$, and a strong fundamental EMIC mode. The results will advance our understanding of harmonic EMIC waves and their generation mechanisms. △ Less

Submitted 20 December, 2024; originally announced December 2024.

Boosting weather forecast via generative superensemble

Authors: Congyi Nai, Xi Chen, Shangshang Yang, Yuan Liang, Ziniu Xiao, Baoxiang Pan

Abstract: Accurate weather forecasting is essential for socioeconomic activities. While data-driven forecasting demonstrates superior predictive capabilities over traditional Numerical Weather Prediction (NWP) with reduced computational demands, its deterministic nature and limited advantages over physics-based ensemble predictions restrict operational applications. We introduce the generative ensemble pred… ▽ More Accurate weather forecasting is essential for socioeconomic activities. While data-driven forecasting demonstrates superior predictive capabilities over traditional Numerical Weather Prediction (NWP) with reduced computational demands, its deterministic nature and limited advantages over physics-based ensemble predictions restrict operational applications. We introduce the generative ensemble prediction system (GenEPS) framework to address these limitations by randomizing and mitigating both random errors and systematic biases. GenEPS provides a plug-and-play ensemble forecasting capability for deterministic models to eliminate random errors, while incorporating cross-model integration for cross-model ensembles to address systematic biases. The framework culminates in a super-ensemble approach utilizing all available data-driven models to further minimize systematic biases. GenEPS achieves an Anomaly Correlation Coefficient (ACC) of 0.679 for 500hPa geopotential (Z500), exceeding the ECMWF Ensemble Prediction System's (ENS) ACC of 0.646. Integration of the ECMWF ensemble mean further improves the ACC to 0.683. The framework also enhances extreme event representation and produces energy spectra more consistent with ERA5 reanalysis. GenEPS establishes a new paradigm in ensemble forecasting by enabling the integration of multiple data-driven models into a high-performing super-ensemble system. △ Less

Submitted 11 December, 2024; originally announced December 2024.

Comments: 21pages, 7figures

Taylor Modeling and Comparative Research Containing Aspect-Ratio Dependent Optimization of Three-Dimensional Hk Superjunction MOSFETs

Authors: Zhentao Xiao, Haimeng Huang, Zonghao Zhang, Chenxing Wang

Abstract: This paper presents a comprehensive study on aspect-ratio dependent optimization for specific on-resistance of three-dimensional high-k superjunction MOSFETs. The research introduces a Taylor modeling method, overcoming the computational limitations of the Bessel method. It also employs the Chynoweth model for more accurate breakdown voltage determination. The study provides a comparative analysis… ▽ More This paper presents a comprehensive study on aspect-ratio dependent optimization for specific on-resistance of three-dimensional high-k superjunction MOSFETs. The research introduces a Taylor modeling method, overcoming the computational limitations of the Bessel method. It also employs the Chynoweth model for more accurate breakdown voltage determination. The study provides a comparative analysis of four different superjunction structures, across five aspects: electric field, impact ionization integral, aspect ratio dependent optimization, charge imbalance effect and temperature. The findings offer valuable insights for the manufacturing guidance of superjunction structure selection △ Less

Submitted 20 November, 2024; originally announced November 2024.

Uncover the Dynamic Community Structure of Instant Delivery Network

Authors: Chengbo Zhang, Yonglin Li, Zuopeng Xiao

Abstract: The rise of instant delivery services has reshaped urban spatial structures through the interaction between suppliers and consumers. However, limited research has explored the spatiotemporal dynamics of delivery network structures. This study constructs a time-dependent, multi-layer instant delivery network in the case city of Beijing using a large-scale dataset from Eleme, organized into 500m gri… ▽ More The rise of instant delivery services has reshaped urban spatial structures through the interaction between suppliers and consumers. However, limited research has explored the spatiotemporal dynamics of delivery network structures. This study constructs a time-dependent, multi-layer instant delivery network in the case city of Beijing using a large-scale dataset from Eleme, organized into 500m grid units. A dynamic community detection method identifies evolving community structures over time. The results reveal 309 dynamic communities, with an average size of 13.78 square kilometers. Communities form in the morning, expand, stabilize, then contract, and disappear by night. Key factors influencing stability include building area and residential population, while online retail and service facilities contribute to instability. These findings offer insights into the spatial structure of instant delivery networks and the factors driving their dynamics, with practical implications for optimizing platform strategies, resource allocation, and urban transportation planning. △ Less

Submitted 15 November, 2024; originally announced November 2024.

Comments: 7 pages, 8 figures.This is a long abstract for participating in the AAG meeting 2025

Design and Process Analysis of a Split-Gate Trench Power MOSFET with Bottom-Trench Hk-Pillar Superjunction for Enhanced Performance

Authors: Yunteng Jiang, Zhentao Xiao, Zonghao Zhang, Juncheng Zhang, Chenxing Wang, Wenjun Li, Haimeng Huang, Aynul Islam, Hongqiang Yang

Abstract: In this paper, we propose a simulation-based novel Split-Gate Trench MOSFET structure with an optimized fabrication process to enhance power efficiency, switching speed, and thermal stability for high-performance semiconductor applications. Integrating high-k pillars with superjunction structures beneath the split gate enhancing breakdown performance by reducing critical field intensity by up to 3… ▽ More In this paper, we propose a simulation-based novel Split-Gate Trench MOSFET structure with an optimized fabrication process to enhance power efficiency, switching speed, and thermal stability for high-performance semiconductor applications. Integrating high-k pillars with superjunction structures beneath the split gate enhancing breakdown performance by reducing critical field intensity by up to 35%, the device achieves a 15% improvement in Figures of Merit (FOMs) for BV2/Ron,sp. Dynamic testing reveals approximately a 25% reduction in both input and output capacitance, as well as gate-to-drain charge (QGD). This reduction, coupled with an approximately 40% improvement in Baliga's High-Frequency Figure of Merit (BHFFOM) and over 20% increase in the New High-Frequency Figure of Merit (NHFFOM), underscores the design's suitability for high-speed, high-efficiency power electronics. Simulations examining the effects of high-k pillar depth indicate that an optimal depth of 3.5 um achieves a balanced performance between BV and Ron,sp. The influence of high-k materials on BT-Hk-SJ MOSFET performance was investigated by comparing hafnium dioxide (HfO2), nitride, and oxynitride. Among these, HfO2 demonstrated optimal performance across static, dynamic, and diode characteristics due to its high dielectric constant, while material choice had minimal impact, with variations kept within 5%. △ Less

Submitted 15 November, 2024; v1 submitted 14 November, 2024; originally announced November 2024.

Comments: 8 pages, 12 figures

TCAD Simulation of Novel Multi-Spacer HK/MG 28nm Planar MOSFET for Sub-threshold Swing and DIBL Optimization

Authors: Zhentao Xiao, Yihao Zheng, Zonghao Zhang, Jinhong Shi, Chenxing Wang, Yunteng Jiang, Haimeng Huang, Aynul Islam, Hongqiang Yang

Abstract: This study optimizes 28 nm planar MOSFET technology to reduce device leakage current and enhance switching speed. The specific aims are to decrease subthreshold swing (S.S.) and mitigate drain induced barrier lowering (DIBL) effect. Silvaco TCAD software is used for process (Athena) and device (Atlas) simulations. For the further development of MOSFET technology, we implemented our device (planar… ▽ More This study optimizes 28 nm planar MOSFET technology to reduce device leakage current and enhance switching speed. The specific aims are to decrease subthreshold swing (S.S.) and mitigate drain induced barrier lowering (DIBL) effect. Silvaco TCAD software is used for process (Athena) and device (Atlas) simulations. For the further development of MOSFET technology, we implemented our device (planar 28 nm n-MOSFET) with high-k metal-gate (HK/MG), lightly doped drain (LDD), multiple spacers (mult-spacers), and silicide. Simulation validation shows improvements over other 28 nm devices, with lower static power consumption and notable optimizations in both S.S. (69.8 mV/dec) and DIBL effect (30.5 mV/V). △ Less

Submitted 8 May, 2025; v1 submitted 23 September, 2024; originally announced September 2024.

Mobility-GCN: a human mobility-based graph convolutional network for tracking and analyzing the spatial dynamics of the synthetic opioid crisis in the USA, 2013-2020

Authors: Zhiyue Xia, Kathleen Stewart

Abstract: Synthetic opioids are the most common drugs involved in drug-involved overdose mortalities in the U.S. The Center for Disease Control and Prevention reported that in 2018, about 70% of all drug overdose deaths involved opioids and 67% of all opioid-involved deaths were accounted for by synthetic opioids. In this study, we investigated the spread of synthetic opioids between 2013 and 2020 in the U.… ▽ More Synthetic opioids are the most common drugs involved in drug-involved overdose mortalities in the U.S. The Center for Disease Control and Prevention reported that in 2018, about 70% of all drug overdose deaths involved opioids and 67% of all opioid-involved deaths were accounted for by synthetic opioids. In this study, we investigated the spread of synthetic opioids between 2013 and 2020 in the U.S. We analyzed the relationship between the spatiotemporal pattern of synthetic opioid-involved deaths and another key opioid, heroin, and compared patterns of deaths involving these two types of drugs during this period. Spatial connections and human mobility between counties were incorporated into a graph convolutional neural network model to represent and analyze the spread of synthetic opioid-involved deaths in the context of previous heroin-involved death patterns. △ Less

Submitted 10 October, 2024; v1 submitted 15 September, 2024; originally announced September 2024.

Comments: Upon further review, my co-authors and I have realized that the paper is a working draft and not yet ready for public dissemination. We plan to continue refining the content and addressing certain issues before resubmitting the paper for consideration in its final form