Showing 1–50 of 161 results for author: Wei, X

A Non-Hermitian Biorthogonal Encoding Paradigm for Physical-Layer Secure Computational Imaging

Authors: Xi-Hao Chen, Kan-Xu Jia, En-Rui Zhang, Yi-Zhu Zhang, Xin-Peng Wei, Bu-Ran Yu, Qian-Qian Bao, Shao-Ying Meng

Abstract: The conventional paradigm of computational imaging, rooted in Hermitian systems, is fundamentally constrained by rigid orthogonal basis transformations, which bottleneck the balance between reconstruction fidelity, computational load, and physical-layer security. In this work, we propose a generalized secure computational imaging framework based on non-Hermitian biorthogonal symmetry breaking. By… ▽ More The conventional paradigm of computational imaging, rooted in Hermitian systems, is fundamentally constrained by rigid orthogonal basis transformations, which bottleneck the balance between reconstruction fidelity, computational load, and physical-layer security. In this work, we propose a generalized secure computational imaging framework based on non-Hermitian biorthogonal symmetry breaking. By mapping spatial information into a biorthogonal operator space, we establish an asymmetric sensing architecture governed by distinct left-basis $\langleφ_{m}\vert{}$ and right-basis $\vert{}ψ_{n}\rangle$ modes, satisfying the biorthogonality relation $\langleφ_{m}\vert{}ψ_{n}\rangle = δ_{mn}$. Within this manifold, precise tuning of the non-Hermitian parameter $γ$ establishes a physical-layer cryptographic gate, where high-fidelity retrieval is exclusively enabled by matching the dual basis; any parameter mismatch triggers deterministic inter-modal crosstalk that effectively neutralizes unauthorized access. Notably, this architecture intrinsically supports direct, iteration-free image retrieval across a wide range of sampling ratios, significantly reducing the computational overhead compared to conventional iterative reconstruction. We validate this framework on a single-pixel imaging platform, demonstrating a fundamental paradigm shift: by embedding security directly into the measurement physics, we transform image retrieval from a software-dependent task into a parameter-sensitive physical decryption process that ensures architecture-intrinsic confidentiality. △ Less

Submitted 20 August, 2026; originally announced August 2026.

A Fixed-Point Neural Operator for Size- and Functional-Transferable Hamiltonian Prediction

Authors: Yunhong Lou, Xihang Yue, Xinran Wei, Tianqi Deng, Linchao Zhu

Abstract: Predicting the Kohn-Sham Hamiltonian with machine learning can accelerate density functional theory while retaining access to molecular orbitals, energy levels, and electronic-structure observables that energy-only surrogates cannot resolve. Yet element-wise agreement with the converged Hamiltonian, an implicit fixed point of the self-consistent field iteration, does not determine the occupied sub… ▽ More Predicting the Kohn-Sham Hamiltonian with machine learning can accelerate density functional theory while retaining access to molecular orbitals, energy levels, and electronic-structure observables that energy-only surrogates cannot resolve. Yet element-wise agreement with the converged Hamiltonian, an implicit fixed point of the self-consistent field iteration, does not determine the occupied subspace that governs orbital energies and densities. Here we present HamEvo, a neural operator that learns the single-step self-consistent update and returns the converged Hamiltonian as its fixed point. HamEvo is pre-trained on intermediate self-consistent trajectories and calibrated at equilibrium with density-matrix supervision. Across benchmarks from MD17 to drug-like QMugs, HamEvo lowers Hamiltonian errors by 35-49% over direct-regression and deep-equilibrium baselines, and predicts QMugs HOMO and LUMO energies with mean absolute errors of 0.036 and 0.053 eV, near the 1 kcal/mol chemical-accuracy scale. Few-shot fine-tuning with only 20 reference conformations extends HamEvo to molecules of up to 122 atoms, well beyond the size range covered by pre-training. With thermal molecular-dynamics sampling, HamEvo captures temperature-dependent HOMO-LUMO gap renormalization beyond the harmonic approximation. Inference is up to 242 times faster than conventional DFT. △ Less

Submitted 11 August, 2026; v1 submitted 12 June, 2026; originally announced June 2026.

Comments: 30 pages, 5 figures, 2 tables

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

Accelerating Locality-Driven Integration in Quantum Chemistry with Block-Structured Matrix Multiplication

Authors: Xinran Wei, Yan Pan, Fusong Ju, Zehao Zhou, Yihong Zhang, Lin Huang, Jianwei Zhu, Jia Zhang, Huanhuan Xia, Bin Shao, Tao Qin

Abstract: Locality-driven integration is a pervasive computational pattern in quantum chemistry, arising whenever spatially localized basis functions interact through numerical quadrature or integral screening. The dominant matrix multiplications in these tasks exhibit dynamic, structured sparsity driven by spatial locality, posing significant challenges for both dense batched kernels and generic sparse for… ▽ More Locality-driven integration is a pervasive computational pattern in quantum chemistry, arising whenever spatially localized basis functions interact through numerical quadrature or integral screening. The dominant matrix multiplications in these tasks exhibit dynamic, structured sparsity driven by spatial locality, posing significant challenges for both dense batched kernels and generic sparse formats on GPUs. We present KerneLDI, a GPU-oriented framework that addresses this regime by co-designing data layout, screening logic, and matrix-computation operators to realize block-structured matrix multiplication for locality-driven integration. KerneLDI reorganizes operand matrices into a unified block-filtered representation that retains only spatially relevant blocks, and executes the resulting contractions with customized dense block multipliers that adapt proven dense-matmul optimizations to retained block pairs. We develop and evaluate KerneLDI on exchange--correlation (EXC) integration in Kohn--Sham density functional theory, a representative and computationally critical instance of this pattern. Across diverse molecular systems, KerneLDI preserves numerical accuracy while delivering up to 10$\times$ speedup for EXC evaluation over a dense GPU baseline, scales favorably with increasing system size and multi-GPU parallelism, accelerates end-to-end self-consistent field calculations, and yields nearly 6$\times$ throughput improvement for ab initio molecular dynamics. △ Less

Submitted 13 May, 2026; v1 submitted 11 May, 2026; originally announced May 2026.

FusionRCG: Orchestrating Recursive Computation Graphs across GPU Memory Hierarchies

Authors: Yihong Zhang, Xinran Wei, Junshi Chen, Fusong Ju, Wei Hu, Jinlong Yang, Huanhuan Xia

Abstract: Evaluating high-dimensional integrals via deep hierarchical recurrences is a dominant cost in quantum chemistry. While CPUs manage these efficiently, GPUs suffer a critical mismatch: limited per-thread memory is quickly overwhelmed by an explosion of simultaneously live intermediate variables. As recurrence scales, this forces massive data spilling to global memory, collapsing performance into a s… ▽ More Evaluating high-dimensional integrals via deep hierarchical recurrences is a dominant cost in quantum chemistry. While CPUs manage these efficiently, GPUs suffer a critical mismatch: limited per-thread memory is quickly overwhelmed by an explosion of simultaneously live intermediate variables. As recurrence scales, this forces massive data spilling to global memory, collapsing performance into a severe memory-bound regime. We present FusionRCG, a framework that jointly optimizes computation graph structure and GPU memory mapping. Exploiting the inherent topological flexibility of recurrence graphs, using electron repulsion integrals as an example, we contribute: (1) liveness-aware graph orchestration to minimize peak live intermediates; (2) algebraic dimensionality reduction via stepwise Cartesian-to-spherical fusion, shrinking intermediate footprints by up to $7.7\times$; and (3) an adaptive multi-tier kernel architecture routing graphs across the memory hierarchy. Evaluated on NVIDIA A100 GPUs, FusionRCG achieves up to $3.09\times$ end-to-end SCF speedup over GPU4PySCF and maintains $75\%$ parallel efficiency at 64~GPUs, successfully rescuing these workloads from memory-bound limits. △ Less

Submitted 13 May, 2026; v1 submitted 11 May, 2026; originally announced May 2026.

Real-space imaging reveals symmetry-selected nonlinear energy routing in a mechanical resonator

Authors: Ya Zhang, Yuko Terasawa, Qian Liu, Shumpei Takenaka, Hua Li, Yutao Xu, Xueyong Wei, Kazuhiko Hirakawa

Abstract: Nonlinear energy routing among modes underlies phenomena ranging from internal resonance and wave mixing to frequency-comb generation in micro- and nanoelectromechanical resonators, yet modal interactions are typically inferred from spectra rather than imaged in real space. This leaves unresolved how energy is spatially routed and what determines which pathways are selected. Here, we use phase-loc… ▽ More Nonlinear energy routing among modes underlies phenomena ranging from internal resonance and wave mixing to frequency-comb generation in micro- and nanoelectromechanical resonators, yet modal interactions are typically inferred from spectra rather than imaged in real space. This leaves unresolved how energy is spatially routed and what determines which pathways are selected. Here, we use phase-locked multi-harmonic stroboscopic interferometry to reconstruct harmonic-resolved differential displacement maps in a nearly mirror-symmetric microelectromechanical resonator. These maps reveal that harmonics generated by a driven mode can be carried by distinct spatial eigenmodes, directly resolving pathways of nonlinear energy transfer. We further show that such mode-selective routing occurs even away from integer frequency matching: generated harmonics are dominated by eigenmodes sharing the driven mode's mirror parity, whereas spectrally closer opposite-parity modes remain strongly suppressed. A nonlinear modal framework links this hierarchy to symmetry-dependent modal-overlap integrals. These results identify spatial symmetry as a selection rule for nonlinear energy routing. △ Less

Submitted 29 June, 2026; v1 submitted 2 May, 2026; originally announced May 2026.

Comments: 21 pages, 5 figures

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.

Behavioral-Level Simulation of Digital Readout for COFFEE at LHCb Upstream Pixel Tracker

Authors: Xiaoxu Zhang, Yang Zhou, Xiaomin Wei, Anqi Wang, Leyi Li, Yu Zhao, Zexuan Zhao, Huimin Wu, Mingjie Feng, Lei Zhang, Jianchun Wang, Yiming Li

Abstract: COFFEE series is a HVCMOS pixel sensor using the advanced 55 nm process, currently being developed for the Upstream Pixel (UP) tracker of the LHCb Upgrade II. To ensure that COFFEE will be able to handle the particle hit rates at UP tracker, which reach a maximum of 322.5 MHz/chip, detailed simulation of the digital readout circuitry was performed. Simulation results show that the column-drain rea… ▽ More COFFEE series is a HVCMOS pixel sensor using the advanced 55 nm process, currently being developed for the Upstream Pixel (UP) tracker of the LHCb Upgrade II. To ensure that COFFEE will be able to handle the particle hit rates at UP tracker, which reach a maximum of 322.5 MHz/chip, detailed simulation of the digital readout circuitry was performed. Simulation results show that the column-drain readout mechanism achieves nearly 100\% efficiency when the single readout cycle does not exceed 100 ns. Meanwhile, the buffer depth and memory resources required for the peripheral readout adapted to the BXID-sharing data format are also evaluated. These provide guidance for the design of COFFEE. The column-drain readout mechanism was used in COFFEE3 (fabricated in 2025), while the peripheral readout architecture adapted to the BXID-sharing data format is implemented in CHiR (taped out in early 2026). △ Less

Submitted 8 April, 2026; originally announced April 2026.

Topological Anderson Random Laser

Authors: Hang-Zheng Shen, Xian-Hao Wei, Xi-Wang Luo, Zheng-Wei Zhou

Abstract: Topological lasers and random lasers embody two contrasting strategies for disorder management in photonics: the former suppresses disorder via protected edge transport, while the latter exploits multiple scattering for feedback. Here, we theoretically demonstrate that these seemingly incompatible paradigms can be unified through a topological Anderson random laser (TARL), where disorder itself in… ▽ More Topological lasers and random lasers embody two contrasting strategies for disorder management in photonics: the former suppresses disorder via protected edge transport, while the latter exploits multiple scattering for feedback. Here, we theoretically demonstrate that these seemingly incompatible paradigms can be unified through a topological Anderson random laser (TARL), where disorder itself induces a topological phase that enables robust lasing. Starting from a trivial photonic lattice, we show that engineered disorder drives the system into a topological Anderson insulator regime, generating emergent chiral edge states that serve as boundary-selective lasing channels. Remarkably, the TARL exhibits rapid mode selection toward a single edge state, producing an ultranarrow emission spectrum and enhanced slope efficiency optimized near disorder strength with maximal topological mobility gap. Furthermore, they exhibit single-mode-like coherence properties, deviating from Kardar-Parisi-Zhang behavior in conventional chiral topological lasers, while remaining significantly more robust against local perturbations than conventional random lasers. Our findings establish a disorder-enabled flexible route to topologically protected single-mode lasing and introduce a fundamentally new design principle for robust, high-coherence photonic light sources. △ Less

Submitted 22 March, 2026; originally announced April 2026.

Comments: 7 pages, 6 figures, with 5 page supplementary information

GPU Accelerated Minimal Auxiliary Basis Approach TDDFT for Large Organic Molecules

Authors: Zehao Zhou, Xiaojie Wu, Yanheng Li, Xinran Wei, Cheng Fan, Fusong Ju, Qiming Sun, Yi Qin Gao

Abstract: We introduce a GPU-accelerated implementation of time-dependent density functional theory with the minimal auxiliary basis approach (TDDFT-risp) in GPU4PySCF, together with large system demonstrations carried out using the Tamm--Dancoff approximation (TDA-risp). The method combines GPU-accelerated three-center integral evaluation, tensor contractions, exchange-space truncation, omission of hydroge… ▽ More We introduce a GPU-accelerated implementation of time-dependent density functional theory with the minimal auxiliary basis approach (TDDFT-risp) in GPU4PySCF, together with large system demonstrations carried out using the Tamm--Dancoff approximation (TDA-risp). The method combines GPU-accelerated three-center integral evaluation, tensor contractions, exchange-space truncation, omission of hydrogen atoms from the auxiliary basis, and a host memory assisted Davidson solver. On the EXTEST42 benchmark set, a conservative 40 eV exchange cutoff yields excitation-energy errors relative to standard TDA of about 0.03--0.05 eV for low-lying states. For systems of 300 to 3000 atoms, we demonstrate that TDA-risp calculations of 15 low-lying excited states with $ω$B97XD/def2-SVP complete on a single A100 GPU with wall times ranging from minutes to hours. These results position GPU-TDDFT-risp as a practical route toward excited-state calculations for large organic and biomolecular systems with thousands of atoms. △ Less

Submitted 31 March, 2026; originally announced March 2026.

Comments: 21 pages, 12 figures

Low-dimensional geometry learning for turbulence prediction in optimized stellarators

Authors: Xishuo Wei, Handi Huang, Haotian Chen, Hongxuan Zhu, Zhe Bai, Samuel Williams, Zhihong Lin

Abstract: The optimized stellarator is an attractive concept for which the averaged particle radial drift is zero, and the single particle loss can be significantly reduced. But for the reactor design, global physics such as turbulent transport also need to be optimized besides the confined single particle orbit, or properties estimated using local estimations and heuristic formulations. The first-principle… ▽ More The optimized stellarator is an attractive concept for which the averaged particle radial drift is zero, and the single particle loss can be significantly reduced. But for the reactor design, global physics such as turbulent transport also need to be optimized besides the confined single particle orbit, or properties estimated using local estimations and heuristic formulations. The first-principle global transport code is too computationally expensive to integrate into the optimization process. The fast surrogate global transport model based on machine learning is a good alternative choice, but the amount of data required to train the surrogate model is numerous due to the high degree-of-freedom of the stellarator design. The work shows that the stellarator design with quasi-helically(QH) symmetric geometry is approximately distributed in a low dimensional latent space, which can be explicitly found by deep learning. This discovery makes it possible to generate global gyrokinetic simulation data for training surrogate models to directly optimize the stellarator geometry for turbulent transport, energetic particle instability, and MHD modes. Using the low dimensional latent space and data analysis methods, the relation between linear zonal residues and axis-excursion is found, providing a simple guide to optimize low turbulent transport QH stellarators. △ Less

Submitted 4 May, 2026; v1 submitted 18 March, 2026; originally announced March 2026.

Design and First Results of COFFEE3: A 55nm HVCMOS Pixel Sensor Prototype for High-Energy Physics Applications

Authors: Xiaomin Wei, Zijun Xu, Weiguo Lu, Yang Zhou, Zhan Shi, Leyi Li, Xiaoxu Zhang, Pengxu Li, Jianpeng Deng, Yang Chen, Yujie Wang, Zhiyu Xiang, Mei Zhao, Cheng Zeng, Mengke Cai, Boxin Wang, Yuman Cai, Bingchen Yan, Anqi Wang, Yu Zhao, Zexuan Zhao, Zheng Wei, Huimin Wu, Ruiguang Zhao, Hongbo Zhu , et al. (3 additional authors not shown)

Abstract: Motivated by the stringent requirements of the Upstream Pixel (UP) tracker in the LHCb Upgrade II and the Inner Tracking detector (ITK) of the Circular Electron Positron Collider, the COFFEE series of pixel sensor chips have been developed using a 55nm High-Voltage CMOS (HVCMOS) process. The primary objective is to achieve a time resolution of a few nanoseconds under a hit density of up to 100 MHz… ▽ More Motivated by the stringent requirements of the Upstream Pixel (UP) tracker in the LHCb Upgrade II and the Inner Tracking detector (ITK) of the Circular Electron Positron Collider, the COFFEE series of pixel sensor chips have been developed using a 55nm High-Voltage CMOS (HVCMOS) process. The primary objective is to achieve a time resolution of a few nanoseconds under a hit density of up to 100 MHz/cm$^2$, while maintaining fine spatial resolution ($\sim$10 $μ$m) and reasonable power consumption ($<$200 mW/cm$^2$). Building on the process validation of the COFFEE2 prototype, this work presents the design and preliminary test results of COFFEE3-a prototype integrating two distinct readout architectures. Architecture 1, tailored for the current triple-well process, adopts NMOS-only in-pixel circuitry and innovative column-level readout to handle high hit densities. The time walk of pixel-level signal is controlled within 10 ns, and the Time of Arrival (TOA) and Time over Threshold (TOT) are measured with a system clock with the period of 25 ns in peripheral circuits. Architecture 2, developed for future possible processes with p-type buried layer isolation, features pixel-level time measurement and storage. A chip-level Time-to-Digital Converter (TDC) is used and the part of Voltage-Controlled Delay Line (VCDL) is copied in each pixel to get a high time resolution. The TOA resolution is estimated to be 4.2 ns and the TOT resolution 8.4 ns. COFFEE3, with a layout size of 3$\times$4 mm$^2$, was manufactured and has undergone preliminary tests. Charge injection tests for analog circuits, and laser tests for full readout chains, confirm that both architectures operate as expected. Next step work will focus on characterizing key performance such as the timing resolution, radiation hardness, and tracking performance of minimum ionising particles. △ Less

Submitted 18 March, 2026; originally announced March 2026.

Tuning Cu/Diamond Interfacial Thermal Conductance via Nitrogen-Termination Engineering

Authors: Guang Yang, Xinling Tang, Zhongkang Lin, Yulin Gu, Wei Hao, Yujie Du, Xiaoguang Wei

Abstract: Cu-diamond composites are recognized as promising high-thermal-conductivity candidates for electronic cooling, offering tunable properties and competitive cost. However, their performance is significantly limited by the poor Cu/diamond interfacial thermal conductance (ITC). Here, we propose a nitrogen-termination strategy to tune the ITC of Cu/diamond interfaces and unravel atomistic mechanisms by… ▽ More Cu-diamond composites are recognized as promising high-thermal-conductivity candidates for electronic cooling, offering tunable properties and competitive cost. However, their performance is significantly limited by the poor Cu/diamond interfacial thermal conductance (ITC). Here, we propose a nitrogen-termination strategy to tune the ITC of Cu/diamond interfaces and unravel atomistic mechanisms by which nitride interlayers tailor phonon transport. Based on the MACE machine-learning interatomic potential (MLIP) framework, we fine-tune the pre-trained MACE-MPA-0 foundation model by incorporating customized C-N-Cu training datasets. Through MLIP-driven lattice dynamics simulations, we demonstrate that an atomically flat N-termination on diamond enhances the ITC by 21% compared to the bare Cu/diamond interface. Mode-resolved phonon spectroscopy reveals that the LA phonons with frequency above 4 THz and wavevectors near Γ-X and Γ-U directions are selectively modulated by N-termination engineering. Analyses of local vibrational states and interfacial bonding further indicate that the N-termination on diamond tunes the interfacial heat conduction via surficial mass modification and bonding regulation, as evidenced by variations in LDOS overlap and COHP spectra. These findings open venues for tuning heat transfer across Cu/diamond interfaces via non-metallic modification, which avoids the graphitization issues associated with metallic coatings, and provide novel guidelines for upgrading the phonon-mediated heat transfer in Cu-diamond composites. △ Less

Submitted 17 March, 2026; originally announced March 2026.

Suppressed Rupture of Thin Metal Films via van der Waals Epitaxy

Authors: Wenxiang Wang, Jiaxing Wang, Guotong Wang, Zhichao Yan, Chenxiao Jiang, Siqin Zhou, Chuanli Yu, Jianhao Chen, Kun Zheng, Thomas Salez, Xiaoding Wei, Zhaohe Dai

Abstract: Ultrathin metal films exhibit liquid-like instabilities, rupturing via surface diffusion far below their melting points. This behavior constrains thermal budgets for advanced integrated circuits and emerging 2D-crystal devices. Here, we demonstrate that these instabilities can be fundamentally suppressed using graphene as a van der Waals (vdW) template. While conventional 20-nm-thick gold films br… ▽ More Ultrathin metal films exhibit liquid-like instabilities, rupturing via surface diffusion far below their melting points. This behavior constrains thermal budgets for advanced integrated circuits and emerging 2D-crystal devices. Here, we demonstrate that these instabilities can be fundamentally suppressed using graphene as a van der Waals (vdW) template. While conventional 20-nm-thick gold films break up into islands below 300 {\textdegree}C, templated films not only remain stable but also become structurally refined after annealing above 600 {\textdegree}C. This exceptional stability stems from a vdW-mediated crystallographic texture that reorganizes grain boundaries into a mechanically robust network. This mechanism significantly widens the processing window for nanoscale interconnects and enables high-temperature integration of metals with 2D-crystal technologies. △ Less

Submitted 23 February, 2026; originally announced February 2026.

NMRTrans: Structure Elucidation from Experimental NMR Spectra via Set Transformers

Authors: Liujia Yang, Zhuo Yang, Jiaqing Xie, Yubin Wang, Ben Gao, Tianfan Fu, Xingjian Wei, Jiaxing Sun, Jiang Wu, Conghui He, Yuqiang Li, Qinying Gu

Abstract: Nuclear Magnetic Resonance (NMR) spectroscopy is fundamental for molecular structure elucidation, yet interpreting spectra at scale remains time-consuming and highly expertise-dependent. While recent spectrum-as-language modeling and retrieval-based methods have shown promise, they rely heavily on large corpora of computed spectra and exhibit notable performance drops when applied to experimental… ▽ More Nuclear Magnetic Resonance (NMR) spectroscopy is fundamental for molecular structure elucidation, yet interpreting spectra at scale remains time-consuming and highly expertise-dependent. While recent spectrum-as-language modeling and retrieval-based methods have shown promise, they rely heavily on large corpora of computed spectra and exhibit notable performance drops when applied to experimental measurements. To address these issues, we build NMRSpec, a large-scale corpus of experimental $^1$H and $^{13}$C spectra mined from chemical literature, and propose NMRTrans, which models spectra as unordered peak sets and aligns the model's inductive bias with the physical nature of NMR. To our best knowledge, NMRTrans is the first NMR Transformer trained solely on large-scale experimental spectra and achieves state-of-the-art performance on experimental benchmarks, improving Top-10 Accuracy over the strongest baseline by +17.82 points (61.15% vs. 43.33%), and underscoring the importance of experimental data and structure-aware architectures for reliable NMR structure elucidation. △ Less

Submitted 9 February, 2026; originally announced February 2026.

Lagged backward-compatible physics-informed neural networks for unsaturated soil consolidation analysis

Authors: Dong Li, Shuai Huang, Yapeng Cao, Yujun Cui, Xiaobin Wei, Hongtao Cao

Abstract: This study develops a Lagged Backward-Compatible Physics-Informed Neural Network (LBC-PINN) for simulating and inverting one-dimensional unsaturated soil consolidation under long-term loading. To address the challenges of coupled air and water pressure dissipation across multi-scale time domains, the framework integrates logarithmic time segmentation, lagged compatibility loss enforcement, and seg… ▽ More This study develops a Lagged Backward-Compatible Physics-Informed Neural Network (LBC-PINN) for simulating and inverting one-dimensional unsaturated soil consolidation under long-term loading. To address the challenges of coupled air and water pressure dissipation across multi-scale time domains, the framework integrates logarithmic time segmentation, lagged compatibility loss enforcement, and segment-wise transfer learning. In forward analysis, the LBC-PINN with recommended segmentation schemes accurately predicts pore air and pore water pressure evolution. Model predictions are validated against finite element method (FEM) results, with mean absolute errors below 1e-2 for time durations up to 1e10 seconds. A simplified segmentation strategy based on the characteristic air-phase dissipation time improves computational efficiency while preserving predictive accuracy. Sensitivity analyses confirm the robustness of the framework across air-to-water permeability ratios ranging from 1e-3 to 1e3. △ Less

Submitted 2 February, 2026; originally announced February 2026.

A 10 Megahertz Spatial Light Modulator

Authors: Xin Wei, Zeyang Li, Abhishek V. Karve, Adam L. Shaw, David I. Schuster, Jonathan Simon

Abstract: Rapid and programmable shaping of light fields is central to modern microscopy, display technologies, optical communications and sensing, quantum engineering, and quantum information processing. Current wavefront shaping technologies face a fundamental dichotomy: liquid-crystal-on-silicon spatial light modulators (LCoS-SLMs) offer high pixel count but suffer from low refresh rates, while acousto-o… ▽ More Rapid and programmable shaping of light fields is central to modern microscopy, display technologies, optical communications and sensing, quantum engineering, and quantum information processing. Current wavefront shaping technologies face a fundamental dichotomy: liquid-crystal-on-silicon spatial light modulators (LCoS-SLMs) offer high pixel count but suffer from low refresh rates, while acousto-optic deflectors (AODs) provide moderate speed with restricted optical beam geometries. Though recent advances in photonic integrated circuits achieve fast switching, there is currently no tool that provides MHz-rate, continuous motion, and arbitrarily reconfigurable control over a set of diffraction-limited spots. Here we introduce a new class of spatial light modulator that provides both 2D pixel geometry and high speed. The device operates by encoding spatial information in frequency bins via a broadband optical phase modulator, and decoding them via a first-of-its-kind, high-resolution 2D spectrometer. The spectrometer, based on the architecture which we call the Re-Imaging Phased Array (RIPA), achieves its sensitivity through long path-lengths, enabled by intra-spectrometer re-imaging lens-guides. We demonstrate site-resolved optical pulsing with a 44(1)~ns rise time, corresponding to frame rates exceeding 10 million frames per second, as well as arbitrary, reconfigurable 2D addressing and multi-site operations, including asynchronous, independent beam motion, splitting, and recombination. Leveraging these tools opens new horizons in rapid optical manipulation of matter across science, from fast, scalable control that approaches the inertial and radiation limits of atoms in quantum processors, to dynamically programmable, microsecond-resolved illumination in microscopy and neuro-biological imaging. △ Less

Submitted 6 July, 2026; v1 submitted 13 January, 2026; originally announced January 2026.

Scalable Machine Learning Force Fields for Macromolecular Systems Through Long-Range Aware Message Passing

Authors: Chu Wang, Lin Huang, Xinran Wei, Tao Qin, Arthur Jiang, Lixue Cheng, Jia Zhang

Abstract: Machine learning force fields (MLFFs) have revolutionized molecular simulations by providing quantum mechanical accuracy at the speed of molecular mechanical computations. However, a fundamental reliance of these models on fixed-cutoff architectures limits their applicability to macromolecular systems where long-range interactions dominate. We demonstrate that this locality constraint causes force… ▽ More Machine learning force fields (MLFFs) have revolutionized molecular simulations by providing quantum mechanical accuracy at the speed of molecular mechanical computations. However, a fundamental reliance of these models on fixed-cutoff architectures limits their applicability to macromolecular systems where long-range interactions dominate. We demonstrate that this locality constraint causes force prediction errors to scale monotonically with system size, revealing a critical architectural bottleneck. To overcome this, we establish the systematically designed MolLR25 ({Mol}ecules with {L}ong-{R}ange effect) benchmark up to 1200 atoms, generated using high-fidelity DFT, and introduce E2Former-LSR, an equivariant transformer that explicitly integrates long-range attention blocks. E2Former-LSR exhibits stable error scaling, achieves superior fidelity in capturing non-covalent decay, and maintains precision on complex protein conformations. Crucially, its efficient design provides up to 30% speedup compared to purely local models. This work validates the necessity of non-local architectures for generalizable MLFFs, enabling high-fidelity molecular dynamics for large-scale chemical and biological systems. △ Less

Submitted 7 January, 2026; originally announced January 2026.

Harnessing Eversion Buckling for Ideal Omnidirectional Energy Absorption

Authors: Junjie Liu, Aijie Tang, Mingchao Liu, Xiaoding Wei, Qingsheng Yang

Abstract: Thin shells can undergo large shape changes governed by the competition between bending and membrane energies. Here, we identify an instability mechanism in everted toroidal shells, referred to as eversion buckling. After eversion, the axisymmetric configuration may either remain stable or lose stability through symmetry breaking, depending on geometry. A scaling analysis reveals a dimensionless p… ▽ More Thin shells can undergo large shape changes governed by the competition between bending and membrane energies. Here, we identify an instability mechanism in everted toroidal shells, referred to as eversion buckling. After eversion, the axisymmetric configuration may either remain stable or lose stability through symmetry breaking, depending on geometry. A scaling analysis reveals a dimensionless parameter that characterizes the ratio between membrane and bending energies. This parameter defines a critical threshold separating a bistable regime, where the axisymmetric everted state persists, from a monostable regime, where the shell collapses into a non-axisymmetric configuration. The transition is consistent with a pitchfork-type bifurcation, leading to collapse without a preferred in-plane direction. Finite element simulations and experiments validate the proposed scaling and the associated stability boundary across different shell geometries. In the bistable regime, individual everted shells exhibit rapid snap-through accompanied by large volumetric contraction and show limited sensitivity of the critical response to boundary constraints. Building on this mechanism, assemblies of such shells form granular systems with a stable stress plateau and high energy absorption efficiency. These results provide a mechanics-based framework for designing shell-based systems with robust and direction-insensitive energy absorption. △ Less

Submitted 3 June, 2026; v1 submitted 22 December, 2025; originally announced December 2025.

A validated lumped-element model for bioinspired acoustic flow sensing toward the performance limit

Authors: Wei Sun, Wanyin Zheng, Xiangyu Wei, David A. Czaplewski, Ronald N. Miles, Jian Zhou

Abstract: Flow sensing is fundamental to both biological survival and technological innovation. Inspired by biological mechanoreceptors, artificial flow sensors detect subtle fluid motion using slender, viscous-driven structures. Among these, acoustic flow sensors that mimic nature's velocity-sensitive ears have the potential to transform vector sound detection. Yet, despite their potential, understanding o… ▽ More Flow sensing is fundamental to both biological survival and technological innovation. Inspired by biological mechanoreceptors, artificial flow sensors detect subtle fluid motion using slender, viscous-driven structures. Among these, acoustic flow sensors that mimic nature's velocity-sensitive ears have the potential to transform vector sound detection. Yet, despite their potential, understanding of how design parameters determine ultimate sensor performance remains limited. To effectively guide flow sensor design, we develop and experimentally validate a lumped-element model that captures the broadband motion of slender microcantilevers immersed in fluid, combining analytical simplicity with quantitative accuracy. The model predicts flow-induced motion, thermomechanical noise, and the minimum detectable signal level, showing strong agreement with experimental measurements in air over a broad frequency range from 100 Hz to 10,000 Hz. This validated model provides a straightforward theoretical framework for designing high-performance micro- and nanomechanical sensors for flow and vector sound detection. △ Less

Submitted 13 November, 2025; originally announced November 2025.

A Second-Order Optical Butterworth Fabry-Pérot Filter

Authors: Zeyang Li, Abhishek V. Karve, Xin Wei, Jonathan Simon

Abstract: Filters with flat-top pass-bands are a key enabling technology for signal processing. From communication to sensing, the ability to choose a pass \emph{band}, rather than a single pass \emph{frequency}, while still efficiently suppressing backgrounds at other frequencies, is a critical capability for ensuring both detection sensitivity and power efficiency. Efficient transmission of a single frequ… ▽ More Filters with flat-top pass-bands are a key enabling technology for signal processing. From communication to sensing, the ability to choose a pass \emph{band}, rather than a single pass \emph{frequency}, while still efficiently suppressing backgrounds at other frequencies, is a critical capability for ensuring both detection sensitivity and power efficiency. Efficient transmission of a single frequency can be achieved by a single-pole resonator -- which in optics is a Fabry-Pérot cavity offering linewidths from kHz to GHz and beyond. Coupling multiple resonators allows for the construction of flat-top multi-pole filters. These, although straightforward from RF to THz where resonators are macroscopic and tunable, are more difficult to control in the optical band and typically realized with dielectric stacks, whose passband widths exceed 100 GHz. Here, we bridge the gap to narrower bandwidth flat-top filters by proposing and implementing a second-order Butterworth-type optical filter in a single two-mirror Fabry-Pérot cavity, by coupling the two polarization modes. We demonstrate a pass-band width of 2.68(1)~GHz, a maximum stopband suppression of 43~dB, and a passband insertion loss of 2.2(1)~dB, with out-of-band power suppression falling as the fourth power of detuning. This approach is viable down to much narrower filters, and has the potential to improve high-frequency phase noise performance of lasers, enhance the sensitivity of LIDARs, and provide higher quality narrowband filtering, for example, for Raman spectroscopy. △ Less

Submitted 28 January, 2026; v1 submitted 16 October, 2025; originally announced October 2025.

Journal ref: Rev. Sci. Instrum. 97, 023103 (2026)

Higher-Order Boundary Conditions for Atomistic Dislocation Simulations

Authors: Xinyi Wei, Julian Braun, Yangshuai Wang, Lei Zhang

Abstract: We present a higher-order boundary condition for atomistic simulations of dislocations that address the slow convergence of standard supercell methods. The method is based on a multipole expansion of the equilibrium displacement, combining continuum predictor solutions with discrete moment corrections. The continuum predictors are computed by solving a hierarchy of singular elliptic PDEs via a Gal… ▽ More We present a higher-order boundary condition for atomistic simulations of dislocations that address the slow convergence of standard supercell methods. The method is based on a multipole expansion of the equilibrium displacement, combining continuum predictor solutions with discrete moment corrections. The continuum predictors are computed by solving a hierarchy of singular elliptic PDEs via a Galerkin spectral method, while moment coefficients are evaluated from force-moment identities with controlled approximation error. A key feature is the coupling between accurate continuum predictors and moment evaluations, enabling the construction of systematically improvable high-order boundary conditions. We thus design novel algorithms, and numerical results for screw and edge dislocations confirm the predicted convergence rates in geometry and energy norms, with reduced finite-size effects and moderate computational cost. △ Less

Submitted 6 October, 2025; originally announced October 2025.

Simulation of radiation environment for the beam monitor of CEE experiment

Authors: Qian Wang, Hulin Wang, Chaosong Gao, Jun Liu, Xianglun Wei, Junshuai Liu, Zhen Wang, Ran Chen, Peng Ma, Haibo Yang, Chengxin Zhao, Mingmei Xu, Shusu Shi, Xiangming Sun, Feng Liu

Abstract: The cooling storage ring external-target experiment is a large-scale nuclear physics experiment, which aims to study the physics of heavy-ion collisions at low temperatures and high baryon densities. A beam monitor (BM) is placed in the beam line to monitor the beam status and to improve the reconstruction resolution of the primary vertices. The radiation dose and particle fluence stemming from th… ▽ More The cooling storage ring external-target experiment is a large-scale nuclear physics experiment, which aims to study the physics of heavy-ion collisions at low temperatures and high baryon densities. A beam monitor (BM) is placed in the beam line to monitor the beam status and to improve the reconstruction resolution of the primary vertices. The radiation dose and particle fluence stemming from the beam interactions with gases and detector materials affect the performance of the sensors and electronics of BM. This paper uses FLUKA Monte Carlo code to simulate the radiation environment of BM detector. Radiation quantities including the total ionizing dose, 1 MeV neutron equivalent fluence, high-energy hadron flux, thermal neutron flux, and nuclear fragment flux are presented. Results of alternative simulation setups, including adding shielding layers inside the BM, are also investigated. △ Less

Submitted 14 September, 2025; originally announced September 2025.

Comments: 15 pages, 12 figures

Journal ref: 2025 JINST 20 P09020

Design and performance of the prototype gaseous beam monitor with GEM and pixel sensors for the CSR external-target experiment

Authors: Hulin Wang, Xianglun Wei, Chaosong Gao, Jun Liu, Junshuai Liu, Zhen Wang, Ran Chen, Bihui You, Peng Ma, Haibo Yang, Chengxin Zhao, Mingmei Xu, Shusu Shi, Guangming Huang, Feng Liu, Xiangming Sun

Abstract: A gaseous beam monitor utilizing gas electron multiplier (GEM) and pixel sensors is being developed for the Cooling Storage Ring (CSR) External-target Experiment (CEE) at Heavy Ion Research Facility in Lanzhou (HIRFL). The beam monitor is mainly used to track each beam particle, providing an accurate reconstruction of the primary vertex of the collision. Two generations of the pixel sensors (named… ▽ More A gaseous beam monitor utilizing gas electron multiplier (GEM) and pixel sensors is being developed for the Cooling Storage Ring (CSR) External-target Experiment (CEE) at Heavy Ion Research Facility in Lanzhou (HIRFL). The beam monitor is mainly used to track each beam particle, providing an accurate reconstruction of the primary vertex of the collision. Two generations of the pixel sensors (named Topmetal-CEE) were produced, with the second generation's performance improving over the first one. The design and performance of the prototype are described in the paper. Characterization of the prototype with heavy-ion beams and laser beams are presented, showing a spatial resolution better than 50 $\mum$ and a time resolution better than 15 ns. △ Less

Submitted 12 September, 2025; originally announced September 2025.

Comments: 6 pages, 5 figures

Journal ref: 2025 JINST 20 C07047

Fluorescence time profile measurement of LAB based liquid scintillator in response to medium relativistic ion particles

Authors: Xiaojie Luo, Shuya Jin, Gaosong Li, Zepeng Li, Fenhua Lu, Yazhou Sun, Shitao Wang, Yaoguang Wang, Yifang Wang, Xiaobao Wei, Liangjian Wen

Abstract: Liquid scintillator is widely used in particle physics experiments due to its high light yield, good timing resolution, scalability and low cost. Certain liquid scintillators exhibit pulse shape discrimination capabilities because of difference in fluorescence timing properties induced by different particles. Its fluoresence timing properties have been measured mostly for radioactive decay sources… ▽ More Liquid scintillator is widely used in particle physics experiments due to its high light yield, good timing resolution, scalability and low cost. Certain liquid scintillators exhibit pulse shape discrimination capabilities because of difference in fluorescence timing properties induced by different particles. Its fluoresence timing properties have been measured mostly for radioactive decay sources at MeV energies. We present a novel measurement of fluorescence time properties of LAB based liquid scintillator in response to high-energy ions of hydrogen (Z = 1), helium (Z = 2) and Krypton at around 200-300 MeV/u for the first time. We compared the results to those from radioactive sources and observed a distinct $dE/dX$ dependence, regardless of the particle type. These findings are essential for physics searches such as the diffuse supernova neutrino background in large liquid scintillator detectors like JUNO, and are also critical towards understanding the underlying scintillation timing mechanism. △ Less

Submitted 11 August, 2025; originally announced August 2025.

Journal ref: JINST 20 (2025) 10, P10042

Tunable Antichiral Hinge State in Photonic Synthetic Dimensions

Authors: Xian-Hao Wei, Xi-Wang Luo, Mu Yang, Yu-Wei Liao, Jin-Shi Xu, Guang-Can Guo, Zheng-Wei Zhou

Abstract: Recent research in 2-dimensional (2D) topological matter has generalized the notion of edge states from chiral to antichiral configurations with the same propagating direction at parallel edges, revealing a rich variety of robust transport phenomena. Here, we propose that antichiral hinge states can emerge in a 3D higher-order topological insulator/semimetal, where two surface/bulk Dirac points ar… ▽ More Recent research in 2-dimensional (2D) topological matter has generalized the notion of edge states from chiral to antichiral configurations with the same propagating direction at parallel edges, revealing a rich variety of robust transport phenomena. Here, we propose that antichiral hinge states can emerge in a 3D higher-order topological insulator/semimetal, where two surface/bulk Dirac points are connected by the hinge states. The band dispersion can be controlled and tilted independently for each hinge using properly designed tunnelings, resulting in tunable antichiral hinge states with programmable propagation direction and velocity. Moreover, we propose experimental realization schemes based on a 1D coupled cavity array with additional synthetic dimensions represented by the photonic orbital angular momentum and frequency. We innovatively introduce both longitudinal and transversal electro-optic modulators to generate the desired tunable tunnelings along the synthetic dimensions, which significantly reduce the experimental complexity by eliminating the need for beam splittings and auxiliary cavities. The tunable antichiral hinge states are confirmed by the photonic transmission spectra. Our work presents the robust and tunable antichiral hinge-state transports which paves the way for exploring novel topological matter and their device applications. △ Less

Submitted 21 June, 2025; originally announced June 2025.

Comments: 12 pages, 6 figures

Accurate and scalable exchange-correlation with deep learning

Authors: Giulia Luise, Chin-Wei Huang, Thijs Vogels, Derk P. Kooi, Sebastian Ehlert, Stephanie Lanius, Klaas J. H. Giesbertz, Amir Karton, Deniz Gunceler, Stefano Battaglia, Gregor N. C. Simm, P. Bernát Szabó, Megan Stanley, Wessel P. Bruinsma, Lin Huang, Xinran Wei, José Garrido Torres, Abylay Katbashev, Rodrigo Chavez Zavaleta, Bálint Máté, Sékou-Oumar Kaba, Roberto Sordillo, Yingrong Chen, David B. Williams-Young, Christopher M. Bishop , et al. (3 additional authors not shown)

Abstract: Density Functional Theory (DFT) underpins much of modern computational chemistry and materials science. Yet, the reliability of DFT-derived predictions of experimentally measurable properties remains fundamentally limited by the need to approximate the unknown exchange-correlation (XC) functional. The traditional paradigm for improving accuracy has relied on increasingly elaborate hand-crafted fun… ▽ More Density Functional Theory (DFT) underpins much of modern computational chemistry and materials science. Yet, the reliability of DFT-derived predictions of experimentally measurable properties remains fundamentally limited by the need to approximate the unknown exchange-correlation (XC) functional. The traditional paradigm for improving accuracy has relied on increasingly elaborate hand-crafted functional forms. This approach has led to a longstanding trade-off between computational efficiency and accuracy, which remains insufficient for reliable predictive modelling of laboratory experiments. Here we introduce Skala, a deep learning-based XC functional that surpasses state-of-the-art hybrid functionals in accuracy across the main-group chemistry benchmark set GMTKN55 with an error of 2.8 kcal/mol, while retaining the lower computational cost characteristic of semi-local DFT. This demonstrated departure from the historical trade-off between accuracy and efficiency is enabled by learning non-local representations of electronic structure directly from data, bypassing the need for increasingly costly hand-engineered features. Leveraging an unprecedented volume of high-accuracy reference data from wavefunction-based methods, we establish that modern deep learning enables systematically improvable neural exchange-correlation models as training datasets expand, positioning first-principles simulations to become progressively more predictive. △ Less

Submitted 21 April, 2026; v1 submitted 17 June, 2025; originally announced June 2025.

Comments: The Skala model and inference code are available under MIT license at https://github.com/microsoft/skala

Soliton self-excitation under pulsed driving in a Kerr resonator

Authors: Matthew Macnaughtan, Zongda Li, Yiqing Xu, Xiaoming Wei, Zhongmin Yang, Stéphane Coen, Miro Erkintalo, Stuart G. Murdoch

Abstract: We present a novel regime of cavity soliton excitation in a Kerr resonator driven by a train of desynchronised pulses. In this regime, the soliton solution is shown to be the sole available state for the intracavity field, allowing for the automatic excitation of single solitons without the application of any external perturbations or parameter ramping. The self-excitation of cavity soliton freque… ▽ More We present a novel regime of cavity soliton excitation in a Kerr resonator driven by a train of desynchronised pulses. In this regime, the soliton solution is shown to be the sole available state for the intracavity field, allowing for the automatic excitation of single solitons without the application of any external perturbations or parameter ramping. The self-excitation of cavity soliton frequency combs is validated through numerical continuation of the Lugiato-Lefever equation, direct numerical integration, and experimental observation. We show that this regime of CS self-excitation requires only the cavity detuning and pump desynchronisation parameters to be set within the correct range, thus considerably simplifying the usually complex task of deterministic cavity soliton excitation. Additionally, we show that this procedure can also be extended to allow the deterministic generation of different families of multi-soliton bound-states. We believe this research offers a promising approach to considerably simplify cavity soliton generation in both macro- and micro- scale Kerr resonators, while also offering greatly increased thermal, power, and nonlinear efficiencies intrinsic to pulsed-driven systems. △ Less

Submitted 11 June, 2025; originally announced June 2025.

Comments: 9 pages, 6 figures

Efficient Seismic Data Interpolation via Sparse Attention Transformer and Diffusion Model

Authors: Xiaoli Wei, Chunxia Zhang, Baisong Jiang, Anxiang Di, Deng Xiong, Jiangshe Zhang, Mingming Gong

Abstract: Seismic data interpolation is a critical pre-processing step for improving seismic imaging quality and remains a focus of academic innovation. To address the computational inefficiencies caused by extensive iterative resampling in current plug-and-play diffusion interpolation methods, we propose the diffusion-enhanced sparse attention transformer (Diff-spaformer), a novel deep learning framework.… ▽ More Seismic data interpolation is a critical pre-processing step for improving seismic imaging quality and remains a focus of academic innovation. To address the computational inefficiencies caused by extensive iterative resampling in current plug-and-play diffusion interpolation methods, we propose the diffusion-enhanced sparse attention transformer (Diff-spaformer), a novel deep learning framework. Our model integrates transformer architectures and diffusion models via a Seismic Prior Extraction Network (SPEN), which serves as a bridge module. Full-layer sparse multi-head attention and feed-forward propagation capture global information distributions, while the diffusion model provides robust prior guidance. To mitigate the computational burden of high-dimensional representations, self-attention is computed along the channel rather than the spatial dimension. We show that using negative squared Euclidean distance to compute sparse affinity matrices better suits seismic data modeling, enabling broader contribution from amplitude feature nodes. An adaptive ReLU function further discards low or irrelevant self-attention values. We conduct training within a single-stage optimization framework, requiring only a few reverse diffusion sampling steps during inference. Extensive experiments demonstrate improved interpolation fidelity and computational efficiency for both random and continuous missing data, offering a new paradigm for high-efficiency seismic data reconstruction under complex geological conditions. △ Less

Submitted 9 June, 2025; originally announced June 2025.

Energy partition in magnetohydrodynamic turbulence

Authors: Xing Wei

Abstract: We use a simple and straightforward method to derive the energy partition in magnetohydrodynamics (MHD) turbulence that was first studied by Lee and then more rigorously by Chandrasekhar. By investigating the energy equation we find that the turbulent viscous and ohmic dissipations are comparable to each other. Under the condition that turbulent viscosity and turbulent magnetic diffusivity are com… ▽ More We use a simple and straightforward method to derive the energy partition in magnetohydrodynamics (MHD) turbulence that was first studied by Lee and then more rigorously by Chandrasekhar. By investigating the energy equation we find that the turbulent viscous and ohmic dissipations are comparable to each other. Under the condition that turbulent viscosity and turbulent magnetic diffusivity are comparable, we deduce that the ratio of kinetic to magnetic energies depends on the ratio of the turbulent magnetic lengthscale to turbulent velocity lengthscale of the largest eddies. When the two largest lengthscales are comparable, the two energies are in equipartition. △ Less

Submitted 6 June, 2025; originally announced June 2025.

Record-high-Q AMTIR-1 microresonators for mid- to long-wave infrared nonlinear photonics

Authors: Liu Yang, Ryo Sugano, Ryomei Takabayashi, Hidetoshi Kanzawa, Hajime Kumazaki, Yongyong Zhuang, Xiaoyong Wei, Takasumi Tanabe, Shun Fujii

Abstract: AMTIR-1 chalcogenide glass has shown its potential for use in thermal imaging systems owing to its low refractive index, thermal resistance and high transparency across the infrared wavelength regime. Here we report a millimeter-scale high-Q whispering gallery mode microresonator made of AMTIR-1. The recorded Q-factor has reached $1.2\times10^7$ at 1550 nm, which is almost two-orders of magnitude… ▽ More AMTIR-1 chalcogenide glass has shown its potential for use in thermal imaging systems owing to its low refractive index, thermal resistance and high transparency across the infrared wavelength regime. Here we report a millimeter-scale high-Q whispering gallery mode microresonator made of AMTIR-1. The recorded Q-factor has reached $1.2\times10^7$ at 1550 nm, which is almost two-orders of magnitude higher than previously reported values. We characterize the thermal properties, where low thermal conductivity plays an important role in thermal resonance tuning. We further show that AMTIR-1 resonators support anomalous dispersion as well as a low absorption coefficient near the 7~\textmu m wavelength band, thus offering the possibility of providing suitable platforms for mid-infrared, long-wave infrared nonlinear optics including microresonator frequency comb generation. △ Less

Submitted 19 September, 2025; v1 submitted 29 May, 2025; originally announced May 2025.

Comments: 6 pages, 5 figures

Journal ref: Opt. Lett. 50, 6554-6557 (2025)

Telecom quantum dots on GaAs substrates as integration-ready high performance single-photon sources

Authors: Beatrice Costa, Bianca Scaparra, Xiao Wei, Hubert Riedl, Gregor Koblmüller, Eugenio Zallo, Jonathan Finley, Lukas Hanschke, Kai Müller

Abstract: The development of deterministic single photon sources emitting in the telecommunication bands is a key challenge for quantum communication and photonic quantum computing. Here, we investigate the optical properties and single-photon emission of molecular beam epitaxy grown semiconductor quantum dots emitting in the telecom O- and C- bands. The quantum dots are embedded in a InGaAs matrix with fix… ▽ More The development of deterministic single photon sources emitting in the telecommunication bands is a key challenge for quantum communication and photonic quantum computing. Here, we investigate the optical properties and single-photon emission of molecular beam epitaxy grown semiconductor quantum dots emitting in the telecom O- and C- bands. The quantum dots are embedded in a InGaAs matrix with fixed indium content grown on top of a compositionally graded InGaAs buffer. This structure allows for the future implementation of electrically contacted nanocavities to enable high-quality and bright QD emission. In detailed optical characterizations we observe linewidths as low as $ 50 μ$eV, close to the spectrometer resolution limit, low fine structure splittings close to $ 10 μ$eV, and $g^{(2)} (0)$ values as low as $0.08$. These results advance the current performance metrics for MBE-grown quantum dots on GaAs substrates emitting in the telecom bands and showcase the potential of the presented heterostructures for further integration into photonic devices. △ Less

Submitted 28 May, 2025; originally announced May 2025.

Comments: 8 pages, 4 figures

Geometry effects on zonal flow dynamics and turbulent transport in optimized stellarators

Authors: Haotian Chen, Xishuo Wei, Hongxuan Zhu, Zhihong Lin

Abstract: Global gyrokinetic simulations find a strong suppression of ion temperature gradient (ITG) turbulence by zonal flows in stellarators optimized for neoclassical transport. The reduction of the ITG transport by the zonal flows in quasi-helicalsymmetric (QH) and quasi-isodynamic (QI) stellarators are much larger than a quasi-axisymmetric (QA) stellarator or a tokamak, thanks to higher linear residual… ▽ More Global gyrokinetic simulations find a strong suppression of ion temperature gradient (ITG) turbulence by zonal flows in stellarators optimized for neoclassical transport. The reduction of the ITG transport by the zonal flows in quasi-helicalsymmetric (QH) and quasi-isodynamic (QI) stellarators are much larger than a quasi-axisymmetric (QA) stellarator or a tokamak, thanks to higher linear residual levels and lower nonlinear frequencies of the zonal flows in the QH and QI. The transport level and energy confinement time in the QH and QI are similar to the tokamak with the same size and temperature gradient, despite the much larger linear growth rates in the stellarators. △ Less

Submitted 27 May, 2025; originally announced May 2025.

Comments: 4 pages, 4 figures,1 table

Higher-order Topological Parity Anomaly and Half-integer Hall Effect in High-dimensional Synthetic Lattices

Authors: Xian-Hao Wei, Xi-Wang Luo, Guang-Can Guo, Zheng-Wei Zhou

Abstract: Recent advances in constructing synthetic dimension provide a powerful tool for exploring exotic topological states of matter in high dimensions. Here we report that the parity anomaly and associated \textit{half-integer} quantized Hall conductance, arising in 2$j$+1 (space-time) dimensions with a single or odd number of Dirac cones, can be realized by the boundary states of $n$-th order topologic… ▽ More Recent advances in constructing synthetic dimension provide a powerful tool for exploring exotic topological states of matter in high dimensions. Here we report that the parity anomaly and associated \textit{half-integer} quantized Hall conductance, arising in 2$j$+1 (space-time) dimensions with a single or odd number of Dirac cones, can be realized by the boundary states of $n$-th order topological insulators in (2$j$+$n$)-dimensional synthetic lattices. We establish a general bulk-boundary correspondence by integrating the ``nested" Wilson loop theory with the time-reversal polarization at highly-symmetric momenta, a set of $Z_2$ topological invariants are extracted which determines the number of higher-order-boundary Dirac cones and their locations. We develop a general construction procedure for Hamiltonians supporting such higher-order topological parity anomaly. Moreover, we propose an experimental implementation scheme based on photonic synthetic dimensions and provide a method for probing the associated half-integer Hall conductance by the transmission spectra. Our work offers the realization and characterization of parity anomaly in general high-dimensional higher-order topological insulators and opens an avenue for exploring fundamental physics and possible device applications enabled by manipulating Dirac cones. △ Less

Submitted 12 May, 2025; originally announced May 2025.

Comments: 5 pages, 4 figures, with 15 page Supplementary information

Full realization of the RIBLL2 separator at the HIRFL-CSR facility

Authors: Xiao-Dong Xu, Yong Zheng, Zhi-Yu Sun, Yu-Nan Song, Bao-Hua Sun, Satoru Terashima, Chang-Jian Wang, Ge Guo, Guang-Shuai Li, Xiu-Lin Wei, Jun-Yao Xu, Ji-Chao Zhang, Yong Cao, Bing-Shui Gao, Jia-Xing Han, Jin-Rong Liu, Chen-Gui Lu, Shu-Ya Jin, Hooi Jin Ong, Hao-Tian Qi, Yun Qin, Ya-Zhou Sun, Isao Tanihata, Lu-Ping Wan, Kai-Long Wang , et al. (11 additional authors not shown)

Abstract: A new experimental platform was constructed at the Second Radioactive Ion Beam Line in Lanzhou (RIBLL2) of HIRFL-CSR accelerator facility at Lanzhou, China. Its performance, along with several newly developed detectors, was tested in two radioactive ion beam experiments utilizing a 400 MeV/u 40Ar beam and a 350 MeV/u 78Kr beam, respectively. The first results from these two experiments demonstrate… ▽ More A new experimental platform was constructed at the Second Radioactive Ion Beam Line in Lanzhou (RIBLL2) of HIRFL-CSR accelerator facility at Lanzhou, China. Its performance, along with several newly developed detectors, was tested in two radioactive ion beam experiments utilizing a 400 MeV/u 40Ar beam and a 350 MeV/u 78Kr beam, respectively. The first results from these two experiments demonstrate a good particle identification capability of the setup, thereby affirming the full realization of the RIBLL2 separator. △ Less

Submitted 30 April, 2025; originally announced May 2025.

Comments: 5 pages, 2 figures

Journal ref: Science Bulletin, 70, 1026-1029, (2025)

DNA Origami Nanostructures Observed in Transmission Electron Microscopy Images can be Characterized through Convolutional Neural Networks

Authors: Xingfei Wei, Qiankun Mo, Chi Chen, Mark Bathe, Rigoberto Hernandez

Abstract: Artificial intelligence (AI) models remain an emerging strategy to accelerate materials design and development. We demonstrate that convolutional neural network (CNN) models can characterize DNA origami nanostructures employed in programmable self-assembling, which is important in many applications such as in biomedicine. Specifically, we benchmark the performance of 9 CNN models -- viz. AlexNet,… ▽ More Artificial intelligence (AI) models remain an emerging strategy to accelerate materials design and development. We demonstrate that convolutional neural network (CNN) models can characterize DNA origami nanostructures employed in programmable self-assembling, which is important in many applications such as in biomedicine. Specifically, we benchmark the performance of 9 CNN models -- viz. AlexNet, GoogLeNet, VGG16, VGG19, ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152 -- to characterize the ligation number of DNA origami nanostructures in transmission electron microscopy (TEM) images. We first pre-train CNN models using a large image dataset of 720 images from our coarse-grained (CG) molecular dynamics (MD) simulations. Then, we fine-tune the pre-trained CNN models, using a small experimental TEM dataset with 146 TEM images. All CNN models were found to have similar computational time requirements, while their model sizes and performances are different. We use 20 test MD images to demonstrate that among all of the pre-trained CNN models ResNet50 and VGG16 have the highest and second highest accuracies. Among the fine-tuned models, VGG16 was found to have the highest agreement on the test TEM images. Thus, we conclude that fine-tuned VGG16 models can quickly characterize the ligation number of nanostructures in large TEM images. △ Less

Submitted 13 March, 2025; originally announced March 2025.

Beam test result and digitization of TaichuPix-3: A Monolithic Active Pixel Sensors for CEPC vertex detector

Authors: Hancen Lu, Tianyuan Zhang, Chang Xu, Shuqi Li, Xinhui Huang, Jia Zhou, Ziyue Yan, Wei Wang, Hao Zeng, Xuewei Jia, Yiming Hu, Xiaoxu Zhang, Zhijun Liang, Wei Wei, Ying Zhang, Xiaomin Wei, Tianya Wu, Lei Zhang, Ming Qi, Jun Hu, Jinyu Fu, Hongyu Zhang, Gang Li, Linghui Wu, Mingyi Dong , et al. (9 additional authors not shown)

Abstract: The Circular Electron-Positron Collider (CEPC), as the next-generation electron-positron collider, is tasked with advancing not only Higgs physics but also the discovery of new physics. Achieving these goals requires high-precision measurements of particles. Taichu seires, Monolithic Active Pixel Sensor (MAPS), a key component of the vertex detector for CEPC was designed to meet the CEPC's require… ▽ More The Circular Electron-Positron Collider (CEPC), as the next-generation electron-positron collider, is tasked with advancing not only Higgs physics but also the discovery of new physics. Achieving these goals requires high-precision measurements of particles. Taichu seires, Monolithic Active Pixel Sensor (MAPS), a key component of the vertex detector for CEPC was designed to meet the CEPC's requirements. For the geometry of vertex detector is long barrel with no endcap, and current silicon lacks a complete digitization model, precise estimation of cluster size particularly causing by particle with large incident angle is needed. Testbeam results were conducted at the Beijing Synchrotron Radiation Facility (BSRF) to evaluate cluster size dependence on different incident angles and threshold settings. Experimental results confirmed that cluster size increases with incident angle. Simulations using the Allpix$^2$ framework replicated experimental trends at small angles but exhibited discrepancies at large angles, suggesting limitations in linear electric field assumptions and sensor thickness approximations. The results from both testbeam and simulations have provided insights into the performance of the TaichuPix chip at large incident angles, offering a crucial foundation for the establishment of a digital model and addressing the estimation of cluster size in the forward region of the long barrel. Furthermore, it offers valuable references for future iterations of TaichuPix, the development of digital models, and the simulation and estimation of the vertex detector's performance. △ Less

Submitted 10 March, 2025; v1 submitted 7 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.

Coupling of dynamical tide and orbital motion

Authors: Xing Wei

Abstract: Dynamical tide consists of various waves that can resonate with orbital motion. We test this coupling of dynamical tide and orbital motion using a simple two-dimensional shallow water model, which can be applied to a rocky planet covered with thin ocean or atmosphere. Then we take the earth-moon system as a fiducial model to calculate the tidal resonances and orbital evolution. We find that tidal… ▽ More Dynamical tide consists of various waves that can resonate with orbital motion. We test this coupling of dynamical tide and orbital motion using a simple two-dimensional shallow water model, which can be applied to a rocky planet covered with thin ocean or atmosphere. Then we take the earth-moon system as a fiducial model to calculate the tidal resonances and orbital evolution. We find that tidal dissipation can even increase with increasing orbital separation because of the coupling of dynamical tide and orbital motion. We draw the conclusion that the coupling is not negligible to study the orbital evolution on secular timescale. △ Less

Submitted 3 February, 2025; originally announced February 2025.

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.

Multiplexed color centers in a silicon photonic cavity array

Authors: Lukasz Komza, Xueyue Zhang, Hanbin Song, Yu-Lung Tang, Xin Wei, Alp Sipahigil

Abstract: Entanglement distribution is central to the modular scaling of quantum processors and establishing quantum networks. Color centers with telecom-band transitions and long spin coherence times are suitable candidates for long-distance entanglement distribution. However, high-bandwidth memory-enhanced quantum communication is limited by high-yield, scalable creation of efficient spin-photon interface… ▽ More Entanglement distribution is central to the modular scaling of quantum processors and establishing quantum networks. Color centers with telecom-band transitions and long spin coherence times are suitable candidates for long-distance entanglement distribution. However, high-bandwidth memory-enhanced quantum communication is limited by high-yield, scalable creation of efficient spin-photon interfaces. Here, we develop a silicon photonics platform consisting of arrays of bus-coupled cavities. The coupling to a common bus waveguide enables simultaneous access to individually addressable cavity-enhanced T center arrays. We demonstrate frequency-multiplexed operation of two T centers in separate photonic crystal cavities. In addition, we investigate the cavity enhancement of a T center through hybridized modes formed between physically distant cavities. Our results show that bus-coupled arrays of cavity-enhanced color centers could enable efficient on-chip and long-distance entanglement distribution. △ Less

Submitted 28 January, 2025; originally announced January 2025.

Anomalous Ultrafast Thermalization of Photoexcited Carriers in Two-Dimensional Materials Induced by Orbital Coupling

Authors: Zhuoqun Wen, Haiyu Zhu, Wenhao Liu, Zhi Wang, Wen Xiong, Xingzhan Wei

Abstract: Understanding the dynamics of photoexcited carriers is essential for advancing photoelectronic device design. Photon absorption generates electron-hole pairs, and subsequent scatterings can induce ultrafast thermalization within a picosecond, forming a quasi-equilibrium distribution with overheated electrons. The high-energy tail of this distribution enables carriers to overcome energy barriers, t… ▽ More Understanding the dynamics of photoexcited carriers is essential for advancing photoelectronic device design. Photon absorption generates electron-hole pairs, and subsequent scatterings can induce ultrafast thermalization within a picosecond, forming a quasi-equilibrium distribution with overheated electrons. The high-energy tail of this distribution enables carriers to overcome energy barriers, thereby enhancing quantum efficiency--a phenomenon known as photo-thermionic emission (PTE). Despite its importance, the onset and mechanisms of PTE remain under debate. Using real-time time-dependent density functional theory (rt-TDDFT), we investigate ultrafast carrier thermalization in two-dimensional materials graphene and PtTe2, and the results reveal distinct differences. In graphene, both electrons and holes thermalize into Fermi-Dirac distributions with good agreement to experiment, while PtTe2 exhibits anomalous high-energy tails for both electrons and holes, deviating significantly from Fermi-Dirac behavior. We attribute this anomaly to differences in orbital coupling between the two materials, from which we derive design principles for identifying optimal PTE candidates and, ultimately, improving photodetector performance. △ Less

Submitted 10 January, 2025; originally announced January 2025.

Comments: 12 pages, 5 figures, 1 table

Gyrokinetic simulations of the effects of magnetic islands on microturbulence in KSTAR

Authors: Xishuo Wei, Javier H Nicolau, Gyungjin Choi, Zhihong Lin, SeongMoo Yang, SangKyeun Kim, WooChang Lee, Chen Zhao, Tyler Cote, JongKyu Park, Dmitri Orlov

Abstract: Gyrokinetic simulations are utilized to study effects of magnetic islands on the ion temperature gradient (ITG) turbulence in the KSTAR tokamak with resonant magnetic perturbations. Simulations show that the transport is controlled by the nonlinear interactions between the ITG turbulence and self-generated vortex flows and zonal flows, leading to an anisotropic structure of fluctuation and transpo… ▽ More Gyrokinetic simulations are utilized to study effects of magnetic islands on the ion temperature gradient (ITG) turbulence in the KSTAR tokamak with resonant magnetic perturbations. Simulations show that the transport is controlled by the nonlinear interactions between the ITG turbulence and self-generated vortex flows and zonal flows, leading to an anisotropic structure of fluctuation and transport on the poloidal plane and in the toroidal direction. Magnetic islands greatly enhance turbulent transport of both particle and heat. The turbulent transport exhibits variations in the toroidal direction, with transport through the resonant layer near the island X-point being enhanced when the X-point is located at the outer mid-plane. A quantitative agreement is shown between simulations and KSTAR experiments in terms of time frequency and perpendicular wavevector spectrum. △ Less

Submitted 8 January, 2025; v1 submitted 12 December, 2024; originally announced December 2024.

Journal ref: Nucl. Fusion 65 026026 (2025)

Research on Composite Bit Technology for Hard Formations and Its Application in Igneous Rock

Authors: Lian Chen, Jiayuan Zhao, Xiaohu Wei, Zhaohui Song, Liyuan Yang, Jintao Zhu

Abstract: The igneous rocks in deep formation have the characteristics of hardness, poor drillability and high abrasiveness, which is a difficulty in speeding up drilling. The drilling efficiency of existing conventional bits is low in igneous rocks. Based on the characteristics of igneous rocks, rock mechanical parameters and drillability experiments of granite, sandstone and other rocks were carried out.… ▽ More The igneous rocks in deep formation have the characteristics of hardness, poor drillability and high abrasiveness, which is a difficulty in speeding up drilling. The drilling efficiency of existing conventional bits is low in igneous rocks. Based on the characteristics of igneous rocks, rock mechanical parameters and drillability experiments of granite, sandstone and other rocks were carried out. The rock drilling experiments of composite bit, tri-cone bit and PDC bit were carried out. Experiments have shown that in granite with very high strength, the drilling efficiency of conventional cone bit is very low, and it is extremely difficult for PDC bit to penetrate. The impact crushing effect of the cone of the composite bit can make the rock at the bottom of the well produce pits and cracks, which can assist the PDC cutters to penetrate into the formation, and solve the problem of the PDC cutters difficulty in penetrating in hard formations. In softer formations, the rock-breaking advantage of composite bit is not obvious, and the rock-breaking efficiency is lower than that of PDC bit. However, in hard formations, the advantage of composite bit is obvious, with higher drilling efficiency than PDC bit and cone bits. The personalized composite bit developed for deep igneous rocks formations has fast drilling speed, strong sustained drilling ability, long footage, and significant drilling speed-up effect. It significantly reduces the number of runs in deep drilling operations and achieves good application results. The composite bit is suitable for drilling in deep igneous hard-to-drill formations, and it has obvious advantages in deep igneous formations. It is a good choice for drilling speed-up in this kind of hard-to-drill formation. △ Less

Submitted 8 December, 2024; originally announced December 2024.

DaYu: Data-Driven Model for Geostationary Satellite Observed Cloud Images Forecasting

Authors: Xujun Wei, Feng Zhang, Renhe Zhang, Wenwen Li, Cuiping Liu, Bin Guo, Jingwei Li, Haoyang Fu, Xu Tang

Abstract: In the past few years, Artificial Intelligence (AI)-based weather forecasting methods have widely demonstrated strong competitiveness among the weather forecasting systems. However, these methods are insufficient for high-spatial-resolution short-term nowcasting within 6 hours, which is crucial for warning short-duration, mesoscale and small-scale weather events. Geostationary satellite remote sen… ▽ More In the past few years, Artificial Intelligence (AI)-based weather forecasting methods have widely demonstrated strong competitiveness among the weather forecasting systems. However, these methods are insufficient for high-spatial-resolution short-term nowcasting within 6 hours, which is crucial for warning short-duration, mesoscale and small-scale weather events. Geostationary satellite remote sensing provides detailed, high spatio-temporal and all-day observations, which can address the above limitations of existing methods. Therefore, this paper proposed an advanced data-driven thermal infrared cloud images forecasting model, "DaYu." Unlike existing data-driven weather forecasting models, DaYu is specifically designed for geostationary satellite observations, with a temporal resolution of 0.5 hours and a spatial resolution of ${0.05}^\circ$ $\times$ ${0.05}^\circ$. DaYu is based on a large-scale transformer architecture, which enables it to capture fine-grained cloud structures and learn fast-changing spatio-temporal evolution features effectively. Moreover, its attention mechanism design achieves a balance in computational complexity, making it practical for applications. DaYu not only achieves accurate forecasts up to 3 hours with a correlation coefficient higher than 0.9, 6 hours higher than 0.8, and 12 hours higher than 0.7, but also detects short-duration, mesoscale, and small-scale weather events with enhanced detail, effectively addressing the shortcomings of existing methods in providing detailed short-term nowcasting within 6 hours. Furthermore, DaYu has significant potential in short-term climate disaster prevention and mitigation. △ Less

Submitted 15 November, 2024; originally announced November 2024.

HiHa: Introducing Hierarchical Harmonic Decomposition to Implicit Neural Compression for Atmospheric Data

Authors: Zhewen Xu, Baoxiang Pan, Hongliang Li, Xiaohui Wei

Abstract: The rapid development of large climate models has created the requirement of storing and transferring massive atmospheric data worldwide. Therefore, data compression is essential for meteorological research, but an efficient compression scheme capable of keeping high accuracy with high compressibility is still lacking. As an emerging technique, Implicit Neural Representation (INR) has recently acq… ▽ More The rapid development of large climate models has created the requirement of storing and transferring massive atmospheric data worldwide. Therefore, data compression is essential for meteorological research, but an efficient compression scheme capable of keeping high accuracy with high compressibility is still lacking. As an emerging technique, Implicit Neural Representation (INR) has recently acquired impressive momentum and demonstrates high promise for compressing diverse natural data. However, the INR-based compression encounters a bottleneck due to the sophisticated spatio-temporal properties and variability. To address this issue, we propose Hierarchical Harmonic decomposition implicit neural compression (HiHa) for atmospheric data. HiHa firstly segments the data into multi-frequency signals through decomposition of multiple complex harmonic, and then tackles each harmonic respectively with a frequency-based hierarchical compression module consisting of sparse storage, multi-scale INR and iterative decomposition sub-modules. We additionally design a temporal residual compression module to accelerate compression by utilizing temporal continuity. Experiments depict that HiHa outperforms both mainstream compressors and other INR-based methods in both compression fidelity and capabilities, and also demonstrate that using compressed data in existing data-driven models can achieve the same accuracy as raw data. △ Less

Submitted 9 November, 2024; originally announced November 2024.

UniTraj: Learning a Universal Trajectory Foundation Model from Billion-Scale Worldwide Traces

Authors: Yuanshao Zhu, James Jianqiao Yu, Xiangyu Zhao, Xun Zhou, Liang Han, Xuetao Wei, Yuxuan Liang

Abstract: Building a universal trajectory foundation model is a promising solution to address the limitations of existing trajectory modeling approaches, such as task specificity, regional dependency, and data sensitivity. Despite its potential, data preparation, pre-training strategy development, and architectural design present significant challenges in constructing this model. Therefore, we introduce Uni… ▽ More Building a universal trajectory foundation model is a promising solution to address the limitations of existing trajectory modeling approaches, such as task specificity, regional dependency, and data sensitivity. Despite its potential, data preparation, pre-training strategy development, and architectural design present significant challenges in constructing this model. Therefore, we introduce UniTraj, a Universal Trajectory foundation model that aims to address these limitations through three key innovations. First, we construct WorldTrace, an unprecedented dataset of 2.45 million trajectories with billions of GPS points spanning 70 countries, providing the diverse geographic coverage essential for region-independent modeling. Second, we develop novel pre-training strategies--Adaptive Trajectory Resampling and Self-supervised Trajectory Masking--that enable robust learning from heterogeneous trajectory data with varying sampling rates and quality. Finally, we tailor a flexible model architecture to accommodate a variety of trajectory tasks, effectively capturing complex movement patterns to support broad applicability. Extensive experiments across multiple tasks and real-world datasets demonstrate that UniTraj consistently outperforms existing methods, exhibiting superior scalability, adaptability, and generalization, with WorldTrace serving as an ideal yet non-exclusive training resource. △ Less

Submitted 29 September, 2025; v1 submitted 6 November, 2024; originally announced November 2024.

Comments: Accepted as a research paper by NeurIPS'25

Measurement of gas properties for the ion-TPC of N$ν$DEx experiment

Authors: Tianyu Liang, Meiqiang Zhan, Hulin Wang, Xianglun Wei, Dongliang Zhang, Jun Liu, Chengui Lu, Qiang Hu, Yichen Yang, Chaosong Gao, Le Xiao, Xiangming Sun, Feng Liu, Chengxin Zhao, Hao Qiu, Kai Chen

Abstract: In the N$ν$DEx collaboration, a high-pressure gas TPC is being developed to search for the neutrinoless double beta decay. The use of electronegative $\mathrm{^{82}SeF_{6}}$ gas mandates an ion-TPC. The reconstruction of $z$ coordinate is to be realized exploiting the feature of multiple species of charge carriers. As the initial stage of the development, we studied the properties of the… ▽ More In the N$ν$DEx collaboration, a high-pressure gas TPC is being developed to search for the neutrinoless double beta decay. The use of electronegative $\mathrm{^{82}SeF_{6}}$ gas mandates an ion-TPC. The reconstruction of $z$ coordinate is to be realized exploiting the feature of multiple species of charge carriers. As the initial stage of the development, we studied the properties of the $\mathrm{SF_{6}}$ gas, which is non-toxic and has similar molecular structure to $\mathrm{SeF_{6}}$. In the paper we present the measurement of drift velocities and mobilities of the majority and minority negative charge carriers found in $\mathrm{SF_{6}}$ at a pressure of 750 Torr, slightly higher than the local atmospheric pressure. The reduced fields range between 3.0 and 5.5 Td. It was performed using a laser beam to ionize the gas inside a small TPC, with a drift length of 3.7 cm. A customized charge sensitive amplifier was developed to read out the anode signals induced by the slowly drifting ions. The reconstruction of $z$ coordinate using the difference in the velocities of the two carriers was also demonstrated. △ Less

Submitted 20 October, 2024; originally announced October 2024.

Comments: 10 pages, 8 figures

Journal ref: Universe 2025, 11, 163

Versatile Full-Field Optical Coherence Tomography with Adjustable Transmission-to-Reflection Ratio and Enhanced Signal-to-Noise Ratio

Authors: Youlong Fan, Qingye Hu, Zhongping Wang, Zengming Zhang, Xiantao Wei

Abstract: Traditional full-field optical coherence tomography (FF-OCT) is effective for rapid cross-sectional imaging but often suffers from incoherent signals due to imbalanced light intensities between the sample and reference arms. While the high-throughput dark-field (HTDF) FF-OCT technique employs an asymmetric beamsplitter (BS) to achieve an asymmetric beam-splitting ratio and optimize the utilization… ▽ More Traditional full-field optical coherence tomography (FF-OCT) is effective for rapid cross-sectional imaging but often suffers from incoherent signals due to imbalanced light intensities between the sample and reference arms. While the high-throughput dark-field (HTDF) FF-OCT technique employs an asymmetric beamsplitter (BS) to achieve an asymmetric beam-splitting ratio and optimize the utilization of available light, the fixed beam-splitting ratio in the optical system limits HTDF FF-OCT to effectively measuring only specific types of samples with certain scattering intensities. To address this limitation, we propose a more versatile FF-OCT system with an adjustable transmission-to-reflection ratio. This system enables accurate measurement across a broader range of samples by optimizing the light source and finely tuning the polarization to achieve the ideal ratio for different materials. We also observed that both signal-to-noise ratio (SNR) and imaging depth are influenced by the beam-splitting ratio. By precisely adjusting the beam-splitting ratio, both SNR and imaging depth can be optimized to achieve their optimal values. △ Less

Submitted 16 October, 2024; originally announced October 2024.

Temporal Correlation between Positive-Charged Cosmic Ray Flux and Solar Polar Field Variation: Insights from Delayed Modulation Analysis

Authors: Shaokun Gong, Linjing Duan, Jiawei Zhao, Xueyu Wei, Jie Feng, Zhibing Li

Abstract: We present an analysis of the time-dependent modulation of galactic cosmic rays near Earth, with a focus on the cosmic proton flux and polar field. Using data from the Alpha Magnetic Spectrometer (AMS) and the Wilcox Solar Observatory, we identify a significant time-lagged relationship between the observation of two missions. Our model incorporates a weighted magnetic field parameter to address th… ▽ More We present an analysis of the time-dependent modulation of galactic cosmic rays near Earth, with a focus on the cosmic proton flux and polar field. Using data from the Alpha Magnetic Spectrometer (AMS) and the Wilcox Solar Observatory, we identify a significant time-lagged relationship between the observation of two missions. Our model incorporates a weighted magnetic field parameter to address the hemispheric asymmetry in polar fields and captures the temporal evolution of cosmic-ray proton spectra in relation to solar activity. We find a time lag of approximately 10 months, varying with cosmic ray rigidity. At 1 GV, the time lag is 360 days, while it is 300 days above 3 GV. A potential mechanism is proposed to explain the observed time-lagged relationship and its dependence on cosmic ray rigidity. This offers predictive insights into cosmic ray modulation within the heliosphere. These results enhance the accuracy of space weather forecasting models, with significant implications for the safety of space missions and aviation. △ Less

Submitted 31 March, 2025; v1 submitted 26 September, 2024; originally announced September 2024.

Comments: 9 pages, 8 figures, journal

Journal ref: Phys. Rev. D 111, 083050, 2025