Showing 1–50 of 198 results for author: Jia, X

TIDE: A Physically Diverse 3D Turbulence Benchmark Dataset for Advancing Scientific Machine Learning

Authors: Yilong Dai, Yiming Sun, Yiheng Chen, Shengyu Chen, Peyman Givi, Xiaowei Jia, Runlong Yu

Abstract: Turbulence is a central testbed for machine learning on physical dynamics because its governing laws are known exactly. However, most existing studies remain in 2D, while 3D turbulence has fundamentally different physics and is far more costly to simulate. Existing 3D resources also typically provide only one realization per configuration, making it difficult to distinguish learning the dynamics f… ▽ More Turbulence is a central testbed for machine learning on physical dynamics because its governing laws are known exactly. However, most existing studies remain in 2D, while 3D turbulence has fundamentally different physics and is far more costly to simulate. Existing 3D resources also typically provide only one realization per configuration, making it difficult to distinguish learning the dynamics from fitting the statistics of a single flow. In this paper, we introduce TIDE (Turbulent Incompressible DNS Ensembles), a 256^3 DNS corpus and benchmark for 3D incompressible turbulence, with 15 configurations on eight controlled axes, independent ensembles, pressure fields, and equation-level verification. The benchmark includes five tasks, standardized learned baselines, controlled generalization splits, and physical-fidelity metrics alongside pointwise error. Across the main forecasting configurations, current learned models barely outperform persistence and still make about twice the error of a spectral solver given the true equations. Moreover, lower pointwise error can coincide with severely distorted small-scale dynamics, showing that accuracy alone does not ensure physical fidelity. Generalization results further show that most regime shifts reflect limited training coverage, whereas forced-to-decay transfer exposes a missing conditioning variable: operators trained under forcing continue to predict driven evolution when the external drive is removed. Closing these accuracy, fidelity, and conditioning gaps is the central open problem made measurable by TIDE. △ Less

Submitted 4 August, 2026; originally announced August 2026.

Comments: 20 pages, 13 figures, 28 tables. Dataset: https://huggingface.co/datasets/ydai17/TIDE ; Code: https://github.com/Dyloong1/TIDE-dataset-benchmark ; DOI: https://doi.org/10.5281/zenodo.21589489

MSC Class: 76F65; 68T07 ACM Class: I.2.6; J.2; I.6.3

Anomalous enhancement of thermal radiation transport by quasidisorder

Authors: Cheng-Long Zhou, Xin-Yu Jia, Shui-Hua Yang, Yan Wang, Yong Zhang, Hong-Liang Yi, Mauro Antezza

Abstract: The transition from order to disorder is conventionally regarded as detrimental to solid-state heat transfer in classical wave and quasiparticle systems. In striking contrast, we show that in near-field thermal radiation, breaking long-range order-shifting from periodic to quasiperiodic configurations-induces a counterintuitive enhancement of energy transport. This effect arises from delocalized i… ▽ More The transition from order to disorder is conventionally regarded as detrimental to solid-state heat transfer in classical wave and quasiparticle systems. In striking contrast, we show that in near-field thermal radiation, breaking long-range order-shifting from periodic to quasiperiodic configurations-induces a counterintuitive enhancement of energy transport. This effect arises from delocalized interactions within quasiperiodic elements, where this quasiperiodicity relays and amplifies thermal electromagnetic energy transfer across large spatial separations, surpassing even corresponding near-field scenarios. The extraordinary transport properties induced by quasidisorder effect of near-field thermal radiation could unlock exciting opportunities for heat flow manipulation, offering transformative implications for thermal science and advancing the fundamental understanding of collective excitations in non-ordered systems. △ Less

Submitted 31 July, 2026; originally announced August 2026.

Journal ref: Physical Review B 113, 245408 (2026)

Geometric Superconducting Diode Effect in an NbN Nanoring

Authors: Tianyu Li, Peiyuan Huang, Jiong Li, Jiyao Shang, Nuo-Zhou Yang, Wuyue Xu, Wen-Cheng Yue, Yang-Yang Lyu, Chong Li, Yihuang Xiong, Xuecou Tu, Tao Tao, Xiaoqing Jia, Qing-Hu Chen, Huabing Wang, Peiheng Wu, Yong-Lei Wang

Abstract: Superconducting diodes, which exhibit nonreciprocal critical currents, are promising building blocks for low-power cryogenic electronics and superconducting circuits. Existing superconducting diode platforms commonly rely on Josephson junctions, multilayer heterostructures, ferromagnetic elements, gate-difined structures. Here, we demonstrate a geometrically induced superconducting diode effect re… ▽ More Superconducting diodes, which exhibit nonreciprocal critical currents, are promising building blocks for low-power cryogenic electronics and superconducting circuits. Existing superconducting diode platforms commonly rely on Josephson junctions, multilayer heterostructures, ferromagnetic elements, gate-difined structures. Here, we demonstrate a geometrically induced superconducting diode effect realized in a structurally minimal, single-materials NbN nanoring, where inversion-symmetry breaking is introduced solely by the asymmetric geometry. The device exhibits pronounced and polarity-switchable critical-current nonreciprocity. Systematic magnetic-field and temperature-dependent measurements reveal that, at low fields, the applied magnetic field redistributes the critical current asymmetrically between opposite bias directions without significantly reducing the overall superconducting current-carrying capability. Moreover, the maximal nonreciprocity and diode efficiency exhibit distinct temperature dependence: the maximal diode efficiency follows the evolution of the energy gap, whereas the maximal nonreciprocity is more closely associated with the superfluid density. These results establish asymmetric superconducting nanorings as a minimal geometric platform for studying nonreciprocal superconducting transport and provide a simple design principle for future superconducting electronics. △ Less

Submitted 22 July, 2026; originally announced July 2026.

Journal ref: Supercond. Sci. Technol. 39 (2026) 085013

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields

Authors: Qiang Liu, Feng Ma, Wei Zhu, Xiyu Jia, Jianmin Xue, Jun Wen, Gaojun Fu

Abstract: Compressible physical fields are widely present in the real physical world, but current artificial intelligence lacks an understanding mechanism for the non-differentiable features in compressible physical fields. Addressing the limitations of existing deep learning architectures in handling global non-differentiable features, we propose the Inverse Low-Dimensional Manifold reconstruction framewor… ▽ More Compressible physical fields are widely present in the real physical world, but current artificial intelligence lacks an understanding mechanism for the non-differentiable features in compressible physical fields. Addressing the limitations of existing deep learning architectures in handling global non-differentiable features, we propose the Inverse Low-Dimensional Manifold reconstruction framework (ILDM). This framework couples the Non-differentiable Approximation Function (NAF) for capturing non-differentiable features in compressible flows with the Smooth Fluid Reconstruction (SFR) module tailored for smooth fluid regions. Extensive evaluations across 1D and 2D benchmarks, including Riemann problems and double Mach reflection, demonstrate that ILDM significantly outperforms cPINN and R-adaptive DeepONet. Specifically, ILDM achieves superior localization of non-differentiable interfaces and maintains robust super-resolution performance even with low-resolution inputs, establishing a physically consistent and scalable paradigm for data-driven fluid dynamics. △ Less

Submitted 8 July, 2026; originally announced July 2026.

Comments: 25 pages with 6 figures

Physics-Preserving Latent Compression for Zero-Shot Resolution Transfer in 3D Turbulence

Authors: Yilong Dai, Yiming Sun, Yiheng Chen, Ziyi Wang, Shengyu Chen, Xiaowei Jia, Runlong Yu

Abstract: High-resolution turbulence modeling is essential for scientific computing, but remains constrained by the cost of direct numerical simulation and the scarcity of full-resolution data. Existing scientific compressors reduce storage but typically operate on per-frame representations, whereas learned compressors yield compact latents that are often resolution-dependent and weakly aligned with the phy… ▽ More High-resolution turbulence modeling is essential for scientific computing, but remains constrained by the cost of direct numerical simulation and the scarcity of full-resolution data. Existing scientific compressors reduce storage but typically operate on per-frame representations, whereas learned compressors yield compact latents that are often resolution-dependent and weakly aligned with the physics of turbulence. This raises the need for a compression framework that reduces data size, preserves physical diagnostics, and transfers from low-resolution training fields to high-resolution test fields without retraining. In this paper, we propose Physics-Preserving Latent Compression (PPLC), a patch-local latent compressor for three-dimensional turbulence. Motivated by inertial-range scale similarity, PPLC treats fixed-size patches as transferable units and applies a shared variational autoencoder independently of the global grid size. It combines exact mean preservation, zero-mean fluctuation encoding, an invertible Haar wavelet front-end, shift-consistency regularization, and overlap-aware reconstruction. Instantiated on forced isotropic turbulence, PPLC is trained only on stride-downsampled 256^3 fields and transfers zero-shot to 1024^3 fields. Experiments show that PPLC improves the balance between reconstruction accuracy and physical fidelity over classical and learned baselines, keeping diagnostics such as dissipation, enstrophy, energy spectra, and incompressibility closer to the ground truth. Beyond turbulence compression, PPLC offers a general strategy for physics-preserving latent representations that support data-efficient scientific surrogate modeling. △ Less

Submitted 19 June, 2026; originally announced June 2026.

Comments: 10 pages, 6 figures, 4 tables

MSC Class: 68T07 (Primary) 76F65; 76F05; 68T05 (Secondary) ACM Class: I.2.6; I.4.2; J.2

LEAP: A Rapid Neural Surrogate of Multi-Fluid MHD at Europa

Authors: Sachin Alexander Reddy, Abigail R. Azari, Corey J. Cochrane, Xianzhe Jia, Tom A. Nordheim, Lukas Mandrake, Steven D. Vance, Camilla Harris, Ioana Ciucǎ

Abstract: Characterizing Europa's subsurface ocean is a key objective of the Europa Clipper and JUICE missions in the search for life beyond Earth. Although the ocean's induced magnetic field provides key constraints on habitability, interpretation is complicated by perturbations arising from Jupiter's plasma interaction with Europa. Physics-based models (e.g. magnetohydrodynamic, MHD) required to character… ▽ More Characterizing Europa's subsurface ocean is a key objective of the Europa Clipper and JUICE missions in the search for life beyond Earth. Although the ocean's induced magnetic field provides key constraints on habitability, interpretation is complicated by perturbations arising from Jupiter's plasma interaction with Europa. Physics-based models (e.g. magnetohydrodynamic, MHD) required to characterize these effects are physically comprehensive, but have a prohibitive computational cost. To address this, we introduce Learning Europa's Atmosphere and Plasma (LEAP), a transformer-based surrogate trained on outputs from a state-of-the-art multi-fluid MHD code to predict magnetic field perturbations along spacecraft trajectories. LEAP evaluates in milliseconds on a laptop, whereas MHD takes 12 hrs on a high-performance computer (~40,000x speed-up). The model has test set errors of -/+ 2.6 nT, and for the Galileo E4 and E14 flybys of Europa it matches the parent MHD model in accuracy. Its enhanced speed enables large-scale parameter surveys and probabilistic estimations of plasma conditions, establishing a new framework for accelerated plasma interaction modeling. LEAP can also inform future MHD simulations while learning from them. Beyond Europa, this framework could be expanded to planning future missions or to other high-priority bodies, including Uranus and Neptune. △ Less

Submitted 8 June, 2026; originally announced June 2026.

Investigating the Uncertainty of Cellular Microenvironment Parameter Estimations via Diffusion MRI Cytometry

Authors: Wen Li, Yan Dai, Arely Perez Rodriguez, Todd Aguilera, Jie Deng, Xun Jia

Abstract: This study aims to identify cell microenvironment parameters that can be robustly estimated from IMPULSED diffusion MRI signals and to develop a reliable mapping-based estimation framework. Diffusion MRI signals were simulated using the established IMPULSED model with one pulsed gradient spin echo sequence and two oscillating gradient spin echo sequences at different frequencies. Five cellular par… ▽ More This study aims to identify cell microenvironment parameters that can be robustly estimated from IMPULSED diffusion MRI signals and to develop a reliable mapping-based estimation framework. Diffusion MRI signals were simulated using the established IMPULSED model with one pulsed gradient spin echo sequence and two oscillating gradient spin echo sequences at different frequencies. Five cellular parameters were considered: cell diameter ($d$), intracellular diffusion coefficient ($D_{in}$), intracellular volume fraction ($V_{in}$), extracellular diffusion coefficient ($D_{ex}$), and the frequency-dependent slope of $D_{ex}$ ($β_{ex}$). Parameter uncertainty was quantified using Jacobian-based sensitivity analysis at an SNR of 30, representing clinically achievable conditions on a 1.5T MRI scanner. To enable direct parameter mapping, signals were logarithmically transformed, reduced in dimension using principal component analysis, and then used to estimate parameters with linear regression, fourth-order polynomial regression, and a fully connected four-layer neural network. Model validation was performed in vitro using MC38 cell lines. Uncertainty analysis identified $d$, $V_{in}$, and $D_{ex}$ as robustly derivable parameters, each with relative uncertainty below 1.0. Among the tested models, the four-layer neural network performed best, with mean absolute errors of 1.7 $μ$m for $d$, 5.06% for $V_{in}$, and 0.28 $μ$m$^2$/ms for $D_{ex}$. In vitro validation showed a 6.7% error in cell diameter estimation. These results demonstrate that IMPULSED dMRI can support robust estimation of key cell microenvironment parameters and provide a practical framework for noninvasive assessment of tumor microenvironment changes during radiation therapy response monitoring. △ Less

Submitted 7 June, 2026; originally announced June 2026.

Comments: 34 pages, 5 figures

A Machine-to-Machine Knowledge-Guided LLM Agent for Generalizable Radiotherapy Treatment Planning

Authors: Md Mainul Abrar, Xun Jia, Yujie Chi

Abstract: In this work, we propose a prototype machine-to-machine (M2M) knowledge-guided Large Language Model (LLM) framework for automated radiotherapy treatment planning. In the proposed paradigm, Treatment Planning Parameter (TPP) distribution knowledge discovered by a Deep Reinforcement Learning (DRL) agent is transferred to an LLM agent through in-context learning, enabling autonomous iterative plannin… ▽ More In this work, we propose a prototype machine-to-machine (M2M) knowledge-guided Large Language Model (LLM) framework for automated radiotherapy treatment planning. In the proposed paradigm, Treatment Planning Parameter (TPP) distribution knowledge discovered by a Deep Reinforcement Learning (DRL) agent is transferred to an LLM agent through in-context learning, enabling autonomous iterative planning without human intervention. While standard LLM-based planning often lacks physical intuition and struggles with convergence, the integration of DRL-derived guidance constrains the agent to a physically valid parameter space. Experimental evaluations are performed across three diverse planning scenarios: basic prostate cases, complex prostate configurations with increased organ-at-risk (OAR) constraints, and liver cases. The evaluation results demonstrate that the guided LLM agent consistently achieves optimal planning scores while significantly reducing the number of iterations compared to unguided planning. Analysis of the final TPP configurations reveals that the agent successfully learns a hierarchical priority of objectives, effectively restoring a logical "cause-and-effect" relationship between parameter tuning and dosimetric outcomes. Crucially, this prototype framework exhibits robust generalizability, maintaining high planning quality regardless of specific patient anatomy, treatment site, or initial plan quality. By bridging the specialized optimization of DRL with the adaptive reasoning of LLMs, this M2M framework establishes a scalable foundation towards generalizable autonomous treatment planning, ultimately benefiting clinical practice in realistic environments. △ Less

Submitted 30 May, 2026; originally announced June 2026.

Comments: 10 pages, 6 figures

MSC Class: 92C50; 68T07 ACM Class: I.2.1; J.3

High-energy topological edge states and strain-induced multiple flat bands in a honeycomb lattice

Authors: Xiaoqin Bai, Haozhen Tian, Boris A. Malomed, Rongcao Yang, Xiaojun Jia

Abstract: We propose a novel anti-twig edge in the honeycomb lattice (HCL) that supports two symmetric high-energy edge states. It is different from the twig edge supporting the zero-energy flat band. Moreover, multiple flat bands are produced by applying a strain to the HCL with a twig edge or an anti-twig edge, and the suppression or enhancement of the high-energy edge state is observed. Under the edge-pa… ▽ More We propose a novel anti-twig edge in the honeycomb lattice (HCL) that supports two symmetric high-energy edge states. It is different from the twig edge supporting the zero-energy flat band. Moreover, multiple flat bands are produced by applying a strain to the HCL with a twig edge or an anti-twig edge, and the suppression or enhancement of the high-energy edge state is observed. Under the edge-parallel stretch strain, the high-energy edge state band merges into the bulk band, suppressing the high-energy edge state, while the zero-energy edge state becomes delocalized. On the other hand, under the action of the edge-parallel compression strain, both the high-energy and zero-energy edge states exhibit strong localization. Pseudo-topological protection of the high-energy edge state is explored too. Finally, by reconstructing the anti-twig edge in the HCL, degenerate flat bands and strain-induced multiple flat bands are produced, and topologically protected vacated anti-twig edge states are demonstrated. △ Less

Submitted 29 May, 2026; originally announced May 2026.

Comments: To be published in Nanophotonics

Laser-driven Ion and Neutron Sources from Medium Repetition Ultrashort PW Laser

Authors: X. Jiao, C. Jeon, G. Tiwari, S. G. Lee, O. Labun, I. W. Choi, L. A. Labun, Mara Klebonas, C. Hojbota, D. D. Phan, C. H. Nam, B. M. Hegelich

Abstract: We report the first experiment investigating ion acceleration and neutron generation irradiating thin plastic targets (CH2) and deuterated plastic targets (CD2) of thickness ranging from 30nm to 160nm using the 4PW (0.1 Hz) laser at CoReLS in South Korea. Thin wedge-shaped filters exploiting differing stopping ranges were designed to distinguish carbon 6+ ions from deuterons in shots with CD2 targ… ▽ More We report the first experiment investigating ion acceleration and neutron generation irradiating thin plastic targets (CH2) and deuterated plastic targets (CD2) of thickness ranging from 30nm to 160nm using the 4PW (0.1 Hz) laser at CoReLS in South Korea. Thin wedge-shaped filters exploiting differing stopping ranges were designed to distinguish carbon 6+ ions from deuterons in shots with CD2 targets. The maximum energies of all ion species from both CH2 and CD2 targets were found to increase linearly with the laser intensity. The highest observed energy of each ion species scales as q (charge)^2/mass, which is more similar to the scaling expected for ponderomotive acceleration than to the scaling expected for TNSA. The maximum ion energies were also found to increase with target thickness. Utilizing the secondary interactions of the deuteron beam, we created a fast neutron source via deuteron breakup reactions on a copper converter. The neutron spectrum follows an exponential distribution with energy up to 15MeV. A neutron yield of 2x10^7n/J was observed from thicker targets, comparable to TNSA-regime laser-driven neutron sources and within one order of magnitude of the highest yields reported using Break-out-afterburner (BOA) acceleration with beryllium converters. △ Less

Submitted 18 May, 2026; originally announced May 2026.

Comments: 13 pages, 6 figures

Large Language Models for AI-Assisted Radiotherapy Scheduling: A Feasibility Study Under Realistic Operational Constraints

Authors: Eric Zhang, Wen Li, Youfang Lai, Annette Souranis, Georgia Paparoidamis, Michael Roumeliotis, Xun Jia

Abstract: Radiotherapy (RT) patient scheduling is a complex operational problem. Current scheduling often relies on manual coordination and can be difficult to adapt to changing clinical demands. This study evaluated the feasibility of using a large language model (LLM) to generate candidate RT patient schedules satisfying predefined clinical and operational constraints. A simulated three-LINAC RT schedulin… ▽ More Radiotherapy (RT) patient scheduling is a complex operational problem. Current scheduling often relies on manual coordination and can be difficult to adapt to changing clinical demands. This study evaluated the feasibility of using a large language model (LLM) to generate candidate RT patient schedules satisfying predefined clinical and operational constraints. A simulated three-LINAC RT scheduling environment was developed over one year using synthetic patient arrivals and treatment characteristics modeled after clinical practice. A total of 1,400 new patients across 12 treatment categories were generated. An LLM-based scheduling framework used structured natural-language prompts encoding clinical rules, operational constraints, and scheduling objectives. Performance was evaluated across scenarios involving weekly time consistency, LINAC continuity, gap-constrained temporal relaxation, and infeasible request handling. Generated schedules were validated using deterministic rule-based checks and manual review. LLM-generated schedules satisfied predefined feasibility rules in the evaluated scenarios. Approximately 99% of evaluated fractions remained within the preferred 60-minute weekly treatment-time window. Adding a LINAC-continuity objective reduced LINAC switching from 54.6% to 10.1%. Adding gap-constrained temporal relaxation reduced Friday mean daily gap time from 169.5 to 89.2 minutes while maintaining approximately 99% of fractions within the 60-minute window. The framework also identified infeasible scheduling requests and proposed interpretable corrective actions. These results suggested that LLMs may support RT patient scheduling in constraint-rich simulated clinical environments, motivating further investigation of LLM-assisted scheduling as a flexible, human-in-the-loop decision-support approach for RT operations. △ Less

Submitted 12 May, 2026; originally announced May 2026.

Comments: 34 pages, 4 figures, 6 tables

How is a gas sensor poisoned by volatile methylsiloxanes?

Authors: Heng Liu, Bingxin Yang, Yiming Lu, Yuan Wang, Xue Jia, Long Luo, Hao Li

Abstract: Volatile methyl siloxanes (VMSs), widely present in consumer and industrial products, have attracted increasing concerns due to their persistence, bioaccumulation behavior, and adverse health effects. Beyond their environmental implications, VMSs also pose operational challenges for sensing technologies because they readily decompose on sensing materials to form silicon-based compounds (e.g., sili… ▽ More Volatile methyl siloxanes (VMSs), widely present in consumer and industrial products, have attracted increasing concerns due to their persistence, bioaccumulation behavior, and adverse health effects. Beyond their environmental implications, VMSs also pose operational challenges for sensing technologies because they readily decompose on sensing materials to form silicon-based compounds (e.g., silica and silane) that irreversibly impair sensing performance, a phenomenon commonly known as siloxane poisoning. Despite its prevalence, the mechanistic basis of this deactivation remains poorly understood. Herein, we present the first comprehensive theoretical study of siloxane-induced poisoning in catalytic gas sensors. Guided by our self-developed AI Agent, Digital Sensor Platform (DigSen), we first identify siloxane poisoning as a previously overlooked yet high-impact research direction. Using hexamethyldisiloxane (HMDS) as a model compound, we then conducted first-principles calculations to uncover decomposition pathways across noble metal surfaces. Strikingly, a descriptor-based microkinetic volcano model is developed to capture the trade-off between sensing activity and resistance to poisoning, enabling predictive identification of anti-poisoning candidates. These insights not only elucidate the origin of siloxane poisoning but also demonstrate how AI-driven discovery, mechanistic theory, and experiments can be integrated into a closed-loop framework for catalytic sensor design. More broadly, this AI-guided paradigm represents a generalizable strategy for materials digital discovery, offering a transferable methodology that extends well beyond siloxane systems to diverse classes of materials challenges. △ Less

Submitted 22 April, 2026; originally announced April 2026.

FlowRefiner: Flow Matching-Based Iterative Refinement for 3D Turbulent Flow Simulation

Authors: Yilong Dai, Yiming Sun, Yiheng Chen, Shengyu Chen, Xiaowei Jia, Runlong Yu

Abstract: Accurate autoregressive prediction of 3D turbulent flows remains challenging for neural PDE solvers, as small errors in fine-scale structures can accumulate rapidly over rollout. In this paper, we propose FlowRefiner, a flow matching-based iterative refinement framework for 3D turbulent flow simulation. The method replaces stochastic denoising refinement with deterministic ODE-based correction, us… ▽ More Accurate autoregressive prediction of 3D turbulent flows remains challenging for neural PDE solvers, as small errors in fine-scale structures can accumulate rapidly over rollout. In this paper, we propose FlowRefiner, a flow matching-based iterative refinement framework for 3D turbulent flow simulation. The method replaces stochastic denoising refinement with deterministic ODE-based correction, uses a unified velocity-field regression objective across all refinement stages, and introduces a decoupled sigma schedule that fixes the noise range independently of refinement depth. These design choices yield stable and effective refinement in the small-noise regime. Experiments on large-scale 3D turbulence with rich multi-scale structures show that FlowRefiner achieves state-of-the-art autoregressive prediction accuracy and strong physical consistency. Although developed for turbulent flow simulation, the proposed framework is broadly applicable to iterative refinement problems in scientific modeling. △ Less

Submitted 24 April, 2026; v1 submitted 18 April, 2026; originally announced April 2026.

Nanoscale electrothermal-switch superconducting diode for electrically programmable superconducting circuits

Authors: Tianyu Li, Jiong Li, Chong Li, Peiyuan Huang, Nuo-Zhou Yang, Wuyue Xu, Wen-Cheng Yue, Yang-Yang Lyu, Yihuang Xiong, Xuecou Tu, Tao Tao, Xiaoqing Jia, Qing-Hu Chen, Huabing Wang, Peiheng Wu, Yong-Lei Wang

Abstract: Superconducting diodes enable dissipationless directional transport, yet achieving electrical tunability and scalability remains a major challenge for circuit-level integration. Here, we demonstrate an electrothermal-switch superconducting diode in which a gate-controlled nanoscale hotspot dynamically breaks inversion symmetry in a superconducting nanowire. This mechanism gives rise to two coexist… ▽ More Superconducting diodes enable dissipationless directional transport, yet achieving electrical tunability and scalability remains a major challenge for circuit-level integration. Here, we demonstrate an electrothermal-switch superconducting diode in which a gate-controlled nanoscale hotspot dynamically breaks inversion symmetry in a superconducting nanowire. This mechanism gives rise to two coexisting nonreciprocal transport regimes-one associated with a nonreciprocal superconducting-to-normal transition and the other with ratchet-like vortex dynamics-both originating from the same electrothermal-switch process. The diode exhibits efficiencies up to 42% and 60% for the two regimes, respectively, and can be electrically switched on, off, or reversed in polarity in situ by applying a small gate current. These capabilities enable programmable superconducting circuits that realize electrically reconfigurable full-wave and half-wave rectification. The lithography-compatible design, high performance, and gate-controlled functionality establish a scalable platform for programmable superconducting electronics and hybrid quantum systems. △ Less

Submitted 14 April, 2026; originally announced April 2026.

Comments: To appear in Nano Letters

Journal ref: Nano Lett. 26, 5411-5417 (2026)

A unified descriptor framework for hydrogen storage capacity and equilibrium pressure in interstitial hydrides

Authors: Seong-Hoon Jang, Di Zhang, Xue Jia, Hung Ba Tran, Linda Zhang, Ryuhei Sato, Yusuke Hashimoto, Yusuke Ohashi, Toyoto Sato, Kiyoe Konno, Shin-ichi Orimo, Hao Li

Abstract: Hydrogen is a promising energy carrier, yet its practical deployment is limited by the lack of storage materials that simultaneously achieve high storage capacity ($w$) and practical equilibrium pressure at room temperature ($P_{\rm eq,RT}$). Interstitial metal hydrides offer fast kinetics and favorable thermodynamics (high $P_{\rm eq,RT}$) but suffer from intrinsically low w. Here, we establish a… ▽ More Hydrogen is a promising energy carrier, yet its practical deployment is limited by the lack of storage materials that simultaneously achieve high storage capacity ($w$) and practical equilibrium pressure at room temperature ($P_{\rm eq,RT}$). Interstitial metal hydrides offer fast kinetics and favorable thermodynamics (high $P_{\rm eq,RT}$) but suffer from intrinsically low w. Here, we establish a physically interpretable, data-driven framework to uncover descriptor-property relationships in interstitial hydrides using a curated database of pressure-composition-temperature measurements (Digital Hydrogen Platform, DigHyd) and white-box symbolic regression. Strikingly, the analysis reveals a clear separation of governing mechanisms, in which $w$ is governed by geometric and lattice conditions, captured by the average atomic radius ($\left\langle r_M \right\rangle$) and average thermal conductivity ($\left\langleκ\right\rangle$), with an optimal regime of $r_M \sim 1.47 Å$ and relatively low $\left\langleκ\right\rangle$. In contrast, $P_{\rm eq,RT}$ is governed by elastic properties, captured by the average shear modulus ($\left\langle G \right\rangle$) and average Poisson's ratio ($\left\langle ν\right\rangle$), reflecting the role of lattice rigidity and mechanical compliance. These relationships are translated into compositional optimization pathways that follow the descriptor trends above, enabling the design of candidate materials with enhanced w under practical equilibrium conditions ($P_{\rm eq,RT} \sim 0.1$ MPa). This work establishes a general, interpretable strategy for physics-informed design of energy materials systems. △ Less

Submitted 13 April, 2026; originally announced April 2026.

Optimizing IMPULSED Acquisition Protocols for Clinical 3T Scanners Through Bayesian Experimental Design

Authors: Yan Dai, Xun Jia, Todd Aguilera, Kai Jiang, Arely Perez Rodriguez, Isabelle Vanhaezebrouck, Jie Deng

Abstract: To optimize diffusion MRI acquisition protocols for IMPULSED model at clinical 3T scanner using Bayesian experimental design, enabling accurate cellular-scale parameter estimation under realistic scan time and scanner hardware constraints. Expected Information Gain (EIG) was used as the optimization objective to maximize the information content of acquired measurements for IMPULSED model fitting.… ▽ More To optimize diffusion MRI acquisition protocols for IMPULSED model at clinical 3T scanner using Bayesian experimental design, enabling accurate cellular-scale parameter estimation under realistic scan time and scanner hardware constraints. Expected Information Gain (EIG) was used as the optimization objective to maximize the information content of acquired measurements for IMPULSED model fitting. Bayesian optimization with Gaussian process surrogates efficiently searched the high-dimensional acquisition parameter space, including pulse types (PGSE, OGSEn1, and OGSEn2), diffusion times, and b-values. Optimized protocols were systematically evaluated against a heuristically designed baseline protocol through simulation studies assessing classification accuracy and parameter estimation performance across SNR levels of 5-40. Robustness to optimization assumptions was examined by varying prior distributions and assumed SNR. In-vivo validation was performed using canine tumor data acquired at 3T. The optimized protocol eliminated OGSEn2 acquisitions, concentrated measurements at high b-values, employing concurrently optimized diffusion timing. Compared to the baseline protocol, the optimized design achieved superior classification accuracy for distinguishing cell populations and reduced parameter estimation error across biologically relevant parameter ranges at various SNRs. Performance advantages were consistent across diverse optimization scenarios, demonstrating robustness to prior knowledge and noise assumptions. In-vivo parameter maps showed substantially improved quality and smoothness. Bayesian optimization substantially improves IMPULSED acquisition design for clinical 3T scanners. This principled, algorithm-agnostic framework enables accurate diffusion MRI cytometry under clinical constraints, with potential applications to tumor characterization and treatment monitoring. △ Less

Submitted 7 April, 2026; originally announced April 2026.

The Bell-Bloom-type optically-pumped FID Rubidium atomic magnetometer with a multi-passing probe beam and two counter-propagating pump beams

Authors: Yongbiao Yang, Zhengyu Su, Yang Li, Yanhua Wang, Jun He, Xiaojun Jia, Junmin Wang

Abstract: The Bell-Bloom-type optically pumped atomic magnetometers are well suited for weak geomagnetic field detection. However, conventional single-beam pumping introduces an atomic spin polarization gradient, which limits the measurement accuracy and sensitivity. To address this issue, this paper proposes and experimentally demonstrates a Bell-Bloom-type rubidium FID magnetometer scheme integrating orth… ▽ More The Bell-Bloom-type optically pumped atomic magnetometers are well suited for weak geomagnetic field detection. However, conventional single-beam pumping introduces an atomic spin polarization gradient, which limits the measurement accuracy and sensitivity. To address this issue, this paper proposes and experimentally demonstrates a Bell-Bloom-type rubidium FID magnetometer scheme integrating orthogonally polarized counter-propagating pumping and multi-pass probe detection. This design homogenizes the atomic spin polarization distribution and suppresses light shifts and power broadening effects induced by the pump beam. Meanwhile, the five-pass probe configuration significantly enhances the signal amplitude. Experimental results reveal that, compared with the traditional single-beam pumping and single-pass detection scheme, the proposed magnetometer achieves a remarkable improvement in magnetic field measurement accuracy, and the magnetic field sensitivity is improved from 18.9 pT/\sqrt{Hz} to 3.1 pT/\sqrt{Hz}. This work provides an effective technical approach and reference for optimizing the performance of atomic magnetometers and extending their applications in integrated arrays. △ Less

Submitted 3 April, 2026; originally announced April 2026.

Conflict Avoidance in Pedestrian Merging in Controlled Experiments by Variance Indicator

Authors: Jiawei Zhang, Xiaolu Jia, Sakurako Tanida, Claudio Feliciani, Daichi Yanagisawa, Katsuhiro Nishinari

Abstract: Pedestrian congestion at corridor intersections often originates from localized fluctuations in motion rather than from a macroscopic collapse of flow. Understanding pedestrian instability at corridor intersections remains challenging because existing studies mainly rely on density, average speed, or flow-based measures and limited datasets, making it difficult to separate geometric turning effect… ▽ More Pedestrian congestion at corridor intersections often originates from localized fluctuations in motion rather than from a macroscopic collapse of flow. Understanding pedestrian instability at corridor intersections remains challenging because existing studies mainly rely on density, average speed, or flow-based measures and limited datasets, making it difficult to separate geometric turning effects from interaction induced fluctuations in merging flows. In particular, the mechanism underlying the turning angle dependence in T junctions has not been resolved. Here, we analyze more than 300 controlled experiments conducted in L corridors with turning only and T corridors with turning and merging. Using Voronoi-based speed variance $V_s$ and velocity variance $V_v$, we systematically compare geometric and interaction effects. $V_s$ effectively captures interaction driven instability, while $V_v$ reflects directional adjustments due to geometry. The comparison reveals distinct fluctuation mechanisms and identifies a critical transition near $90°$, demonstrating the advantage of variance-based indicators for diagnosing pedestrian dynamics. △ Less

Submitted 20 March, 2026; originally announced March 2026.

Effect of vehicle groups on heterogeneous disordered traffic flow

Authors: Akihito Nagahama, Nichika Asai, Claudio Feliciani, Xiaolu Jia, Katsuhiro Nishinari

Abstract: In heterogeneous disordered traffic, where various vehicle types operate without strict lane discipline, self-organized vehicle groups often emerge, but their influence remains unclear. This study examines how group prevalence and composition affect flow-density relationships using real-world trajectory data. Three Passenger Car Unit (PCU) estimation methods construct flow-density diagrams account… ▽ More In heterogeneous disordered traffic, where various vehicle types operate without strict lane discipline, self-organized vehicle groups often emerge, but their influence remains unclear. This study examines how group prevalence and composition affect flow-density relationships using real-world trajectory data. Three Passenger Car Unit (PCU) estimation methods construct flow-density diagrams accounting for heterogeneity. Group proportion, the share of vehicles classified in groups, has a nonlinear, traffic-situation-dependent impact on flow. Moderate group proportions (30-60%) were more clearly associated with peak-flow or high-flow cases under denser traffic conditions, rather than consistently producing the highest representative flow in the median-based analysis. Comparing vehicle-count-based and PCU-based group proportions shows that normalization affects interpretation, particularly when groups consist mainly of small-PCU vehicles such as motorcycles. Lower group proportions were associated with relatively favorable flow under free-flow conditions, while entropy-based analysis shows that the association between entropy alone and speed is not consistent across traffic situations. Contrasting representative trends and extreme high-flow cases suggests that traffic at similar density and group proportion can exhibit low-efficiency and high-efficiency modes. Overall, group prevalence is important in shaping macroscopic traffic dynamics, while linking internal group composition to performance may require richer structural descriptors beyond a single entropy measure. These results imply condition-dependent bottom-up traffic control: encouraging favorable vehicle formations in denser traffic and preserving fluid leader-follower relations in free-flow traffic may improve heterogeneous traffic flow, especially with minimal infrastructure or driver-assistance technologies. △ Less

Submitted 20 August, 2026; v1 submitted 20 February, 2026; originally announced February 2026.

Comments: All of the abstract is in the PDF file

Journal ref: Physica A: Statistical Mechanics and its Applications 700 (2026) 131866

PEST: Physics-Enhanced Swin Transformer for 3D Turbulence Simulation

Authors: Yilong Dai, Shengyu Chen, Xiaowei Jia, Peyman Givi, Runlong Yu

Abstract: Accurate simulation of turbulent flows is fundamental to scientific and engineering applications. Direct numerical simulation (DNS) offers the highest fidelity but is computationally prohibitive, while existing data-driven alternatives struggle with stable long-horizon rollouts, physical consistency, and faithful simulation of small-scale structures. These challenges are particularly acute in thre… ▽ More Accurate simulation of turbulent flows is fundamental to scientific and engineering applications. Direct numerical simulation (DNS) offers the highest fidelity but is computationally prohibitive, while existing data-driven alternatives struggle with stable long-horizon rollouts, physical consistency, and faithful simulation of small-scale structures. These challenges are particularly acute in three-dimensional (3D) settings, where the cubic growth of spatial degrees of freedom dramatically amplifies computational cost, memory demand, and the difficulty of capturing multi-scale interactions. To address these challenges, we propose a Physics-Enhanced Swin Transformer (PEST) for 3D turbulence simulation. PEST leverages a window-based self-attention mechanism to effectively model localized PDE interactions while maintaining computational efficiency. We introduce a frequency-domain adaptive loss that explicitly emphasizes small-scale structures, enabling more faithful simulation of high-frequency dynamics. To improve physical consistency, we incorporate Navier--Stokes residual constraints and divergence-free regularization directly into the learning objective. Extensive experiments on two representative turbulent flow configurations demonstrate that PEST achieves accurate, physically consistent, and stable autoregressive long-term simulations, outperforming existing data-driven baselines. △ Less

Submitted 9 February, 2026; originally announced February 2026.

Manifold Learning with Implicit Physics Embedding for Reduced-Order Flow-Field Modeling

Authors: Weiji Wang, Chunlin Gong, Xuyi Jia, Chunna Li

Abstract: Nonlinear manifold learning (ML) based reduced-order models (ROMs) can substantially improve the quality of nonlinear flow-field modeling. However, noise and the lack of physical information often distort the dimensionality-reduction process, reducing the robustness and accuracy of flow-field prediction. To address this problem, we propose a novel manifold learning ROM with implicit physics embedd… ▽ More Nonlinear manifold learning (ML) based reduced-order models (ROMs) can substantially improve the quality of nonlinear flow-field modeling. However, noise and the lack of physical information often distort the dimensionality-reduction process, reducing the robustness and accuracy of flow-field prediction. To address this problem, we propose a novel manifold learning ROM with implicit physics embedding (IPE-ML). Starting from data-driven manifold coordinates, we incorporate physical parameters (e.g., angle of attack, Mach number) into manifold coordinates system by minimizing the prediction error of Gaussian process regression (GPR) model, thereby fine-tuning the manifold structure. These adjusted coordinates are then used to construct a flow-fields prediction model that predict nonlinear flow-field more accurately. The method is validated on two test cases: transonic flow-field modeling of the RAE2822 and supersonic flow-field modeling of the hexagon airfoil. The results indicate that the proposed IPE-ML can significantly improve the overall prediction accuracy of nonlinear flow fields. In transonic case, shock-related errors have been notably reduced, while in supersonic case the method can confine errors to small local regions. This study offers a new perspective on embedding physical information into nonlinear ROMs. △ Less

Submitted 20 January, 2026; originally announced January 2026.

Human-like AI-based Auto-Field-in-Field Whole-Brain Radiotherapy Treatment Planning With Conversation Large Language Model Feedback

Authors: Adnan Jafar, An Qin, Gavin Atkins, Xiaoyu Hu, Yin Gao, Xun Jia

Abstract: Whole-brain radiotherapy (WBRT) is a common treatment due to its simplicity and effectiveness. While automated Field-in-Field (Auto-FiF) functions assist WBRT planning in modern treatment planning systems, it still requires manual approaches for optimal plan generation including patient-specific hyperparameters definition and plan refinement based on quality feedback. This study introduces an auto… ▽ More Whole-brain radiotherapy (WBRT) is a common treatment due to its simplicity and effectiveness. While automated Field-in-Field (Auto-FiF) functions assist WBRT planning in modern treatment planning systems, it still requires manual approaches for optimal plan generation including patient-specific hyperparameters definition and plan refinement based on quality feedback. This study introduces an automated WBRT planning pipeline that integrates a deep learning (DL) Hyperparameter Prediction model for patient-specific parameter generation and a large-language model (LLM)-based conversational interface for interactive plan refinement. The Hyperparameter Prediction module was trained on 55 WBRT cases using geometric features of clinical target volume (CTV) and organs at risk (OARs) to determine optimal Auto-FiF settings in RayStation treatment planning system. Plans were generated under predicted hyperparameters. For cases in which the generated plan was suboptimal, quality feedback via voice input was captured by a Conversation module, transcribed using Whisper, and interpreted by GPT-4o to adjust planning settings. Plan quality was evaluated in 15 independent cases using clinical metrics and expert review, and model explainability was supported through analysis of feature importance. Fourteen of 15 DL-generated plans were clinically acceptable. Normalized to identical CTV D95% as the clinical plans, the DL-generated and clinical plans showed no statistically significant differences in doses to the eyes, lenses, or CTV dose metrics D1% and D99%. The DL-based planning required under 1 minute of computation and achieved total workflow execution in approximately 7 minutes with a single mouse click, compared to 15 minutes for manual planning. In cases requiring adjustment, the Conversational module successfully improved dose conformity and hotspot reduction. △ Less

Submitted 2 January, 2026; originally announced January 2026.

Comments: 22 pages, 7 figures

Heteronuclear and Homonuclear Vector Solitons in Lasers

Authors: Du Yueqing, Zhang Zhenzhu, Zhang Heze, Xue Jia, Zeng Chao, Cui Yudong, Mao Dong, Malomed Boris, Zhao Jianlin

Abstract: Vector solitons (VSs), being observed across various fields from optics to Bose-Einstein condensates, are localized structures composed of orthogonal modes bound by nonlinear couplings. Nevertheless, the influence of intermodal linear coupling on the physical properties of this bimodal structure remains to be decently revealed and harnessed. Utilizing an ultrafast fiber laser as a platform, we pre… ▽ More Vector solitons (VSs), being observed across various fields from optics to Bose-Einstein condensates, are localized structures composed of orthogonal modes bound by nonlinear couplings. Nevertheless, the influence of intermodal linear coupling on the physical properties of this bimodal structure remains to be decently revealed and harnessed. Utilizing an ultrafast fiber laser as a platform, we predict and demonstrate that the linear mode coupling (LMC) induces the deformable VS in terms of the temporal and spectral structures. Weak LMC supports heteronuclear vector solitons built of dissimilar polarization modes, i.e., a single pulse coupled to an orthogonal damped pulse chain. On the other hand, strong LMC facilitates the homonuclear VS composed of polarization modes with similar structures, in the form of soliton compounds featuring caterpillar motions. Our findings reveal new patterns of VSs and open an effective avenue for versatile ultrafast optical sources. △ Less

Submitted 10 December, 2025; originally announced December 2025.

Comments: To be published by Physical Review Letters

Investigation of the Physical Mechanism behind Retention Loss in FeFETs with MIFIFIS Gate Structure

Authors: Tao Hu, Zeqi Chen, Runhao Han, Xinpei Jia, Jia Yang, Mingkai Bai, Ruoyao Ji, Yajing Ding, Mengwei Zhao, Yuhan Li, Kaiyi Li, Wenbo Fan, Xianzhou Shao, Xiaoqing Sun, Kai Han, Jing Zhang, Yanrong Wang, Junshuai Chai, Hao Xu, Xiaolei Wang, Wenwu Wang, Tianchun Ye

Abstract: A Metal-Gate Blocking Layer (GBL)- Ferroelectric-Tunnel Dielectric Layer (TDL)-Ferroelectric -Channel Insulator (Ch.IL)-Si (MIFIFIS) structure is proposed to achieve a larger MW for applications in Fe-NAND. However, the large retention loss (RL) in the MIFIFIS structure restricts its application. In this work, we vary the physical thickness of the GBL and TDL, and conduct an in-depth analysis of t… ▽ More A Metal-Gate Blocking Layer (GBL)- Ferroelectric-Tunnel Dielectric Layer (TDL)-Ferroelectric -Channel Insulator (Ch.IL)-Si (MIFIFIS) structure is proposed to achieve a larger MW for applications in Fe-NAND. However, the large retention loss (RL) in the MIFIFIS structure restricts its application. In this work, we vary the physical thickness of the GBL and TDL, and conduct an in-depth analysis of the energy bands of the gate structure to investigate the physical mechanism behind the RL in FeFETs with the MIFIFIS structure. The physical origin of the RL is that the electric field direction across the TDL reduces the potential barrier provided by the ferroelectric near the silicon substrate. Based on the above physical mechanism, the RL can be reduced to 12% and 0.2% by redesigning the gate structure or reducing the pulse amplitude, respectively. Our work contributes to a deeper understanding of the physical mechanism behind the RL in FeFETs with the MIFIFIS gate structure. It provides guidance for enhancing the reliability of FeFETs. △ Less

Submitted 4 December, 2025; originally announced December 2025.

Comments: 4 pages, 6 figures

Beam-test evaluation of pre-production Low Gain Avalanche Detectors for the ATLAS High Granularity Timing Detector

Authors: A. Aboulhorma, M. Ait Tamlihat, H. M. Alfanda, O. Atanova, N. Atanov, I. Azzouzi, J. Barreiro Guimarães da Costa, T. Beau, D. Benchekroun, F. Bendebba, G. Bergamin, Y. Bimgdi, A. Blot, A. Boikov, J. Bonis, D. Boumediene, C. Brito, A. S. Brogna, A. M. Burger, L. Cadamuro, Y. Cai, N. Cartalade, R. Casanova Mohr, R. Cherkaoui El Moursli, Y. Che , et al. (207 additional authors not shown)

Abstract: The High Granularity Timing Detector (HGTD) will be installed in the ATLAS experiment as part of the Phase-II upgrade for the High Luminosity-Large Hadron Collider (HL-LHC). It will mitigate pile-up effects in the forward region, and measure per bunch luminosity. The design of HGTD is based on Low Gain Avalanche Detector (LGAD) sensors. This paper presents the results of beam-test campaigns conduc… ▽ More The High Granularity Timing Detector (HGTD) will be installed in the ATLAS experiment as part of the Phase-II upgrade for the High Luminosity-Large Hadron Collider (HL-LHC). It will mitigate pile-up effects in the forward region, and measure per bunch luminosity. The design of HGTD is based on Low Gain Avalanche Detector (LGAD) sensors. This paper presents the results of beam-test campaigns conducted at CERN and DESY in 2023 and 2024 on single LGADs from HGTD pre-production test structures, before and after neutron irradiation up to fluences of $2.5 \times 10^{15}~\mathrm{n_{eq}/cm^2}$. The tested LGADs can meet HGTD requirements in terms of charge collection, time resolution, and hit efficiency, even under HL-LHC end-of-life conditions, supporting their deployment in the final detector. △ Less

Submitted 30 January, 2026; v1 submitted 1 December, 2025; originally announced December 2025.

Towards Human-Centric Intelligent Treatment Planning for Radiation Therapy

Authors: Adnan Jafar, Xun Jia

Abstract: Current radiation therapy treatment planning is limited by suboptimal plan quality, inefficiency, and high costs. This perspective paper explores the complexity of treatment planning and introduces Human-Centric Intelligent Treatment Planning (HCITP), an AI-driven framework under human oversight, which integrates clinical guidelines, automates plan generation, and enables direct interactions with… ▽ More Current radiation therapy treatment planning is limited by suboptimal plan quality, inefficiency, and high costs. This perspective paper explores the complexity of treatment planning and introduces Human-Centric Intelligent Treatment Planning (HCITP), an AI-driven framework under human oversight, which integrates clinical guidelines, automates plan generation, and enables direct interactions with operators. We expect that HCITP will enhance efficiency, potentially reducing planning time to minutes, and will deliver personalized, high-quality plans. Challenges and potential solutions are discussed. △ Less

Submitted 14 October, 2025; originally announced October 2025.

Comments: 27 pages, 3 figures

BigBang-Proton Technical Report: Next-Word-Prediction is Scientific Multitask Learner

Authors: Hengkui Wu, Liujiang Liu, Jihua He, Qihao Wang, Keke Zhao, Shuyang Hu, Renle Fu, Dahao Liang, Lingyu Zeng, Bruce Liu, Yuan Liu, Jin Zhan, Jiaqiang Niu, Xinglong Jia, Yaqin Hu, Wenjun Ji, Panpan Chi, Ken Chen, Hengyuan Wu, Yingsi Xin, Yongfeng Zhu, Yuexin Wang, Manqi Ruan, Ningtao Bian, Xiaohua Wu , et al. (1 additional authors not shown)

Abstract: We introduce BigBang-Proton, a unified sequence-based architecture for auto-regressive language modeling pretrained on cross-scale, cross-structure, cross-discipline real-world scientific tasks to construct a scientific multi-task learner. BigBang-Proton incorporates three fundamental innovations compared to mainstream general-purpose LLMs: Theory-Experiment Learning paradigm aligns large-scale nu… ▽ More We introduce BigBang-Proton, a unified sequence-based architecture for auto-regressive language modeling pretrained on cross-scale, cross-structure, cross-discipline real-world scientific tasks to construct a scientific multi-task learner. BigBang-Proton incorporates three fundamental innovations compared to mainstream general-purpose LLMs: Theory-Experiment Learning paradigm aligns large-scale numerical experimental data with theoretical text corpora; Binary Patch Encoding replaces byte pair encoding(BPE) tokenization; Monte Carlo Attention substitutes traditional transformer architectures. Through next-word-prediction pretraining on cross-discipline scientific datasets of real-world problems mixed with general textual corpus, followed by fine-tuning and inference on downstream tasks, BigBang-Proton demonstrates 100\% accuracy in up to 50-digit arithmetic addition operations, performance on par with leading specialized models in particle physics jet tagging, matching MAE of specialized models in inter-atomic potential simulation, performance comparable to traditional spatiotemporal models in water quality prediction, and benchmark-exceeding performance in genome modeling. These results prove that language-guided scientific computing can match or exceed the performance of task-specific scientific models while maintaining multitask learning capabilities. We further hypothesize to scale the pretraining to the universe scale as a fundamental step toward developing material world foundational model. △ Less

Submitted 30 September, 2025; originally announced October 2025.

Comments: 93 pages, 39 figures

MSC Class: 68T05; 68T50; 00A69; 94A99 ACM Class: I.2.6; I.2.7; J.2; I.6.3; K.4.1

Observation of Iron Oxide to Nitride Conversion via Liquid Liquid Phase Separation in High pressure Borate Melt

Authors: Yu Tao, Depu Liu, Chunyin Zhou, Xu Jia, Jingyi Liu, Xue Chang, Yangbin Wang, Yipeng Wang, Duanwei He, Li Lei

Abstract: High pressure chemistry provides a powerful route to materials that are inaccessible or difficult to synthesize under ambient conditions. However, high pressure chemical reaction processes and mechanisms remain largely unexplored because of the challenges associated with in situ characterization under high pressure and high temperature, particularly within the deeply enclosed sample environment of… ▽ More High pressure chemistry provides a powerful route to materials that are inaccessible or difficult to synthesize under ambient conditions. However, high pressure chemical reaction processes and mechanisms remain largely unexplored because of the challenges associated with in situ characterization under high pressure and high temperature, particularly within the deeply enclosed sample environment of a large volume press. Here, we employ the state of the art real time synchrotron X ray radiography to image a high pressure chemical reaction at 5.4 GPa and 1700 K within a large volume press. Using Fe2O3 and BN as precursors, we capture the complete dynamic metal oxide to nitride conversion and show that it differs fundamentally from conventional solid state diffusion controlled nitridation. Synchrotron X ray radiography clearly revealed that this conversion involves a two stage liquid liquid separation process (LLPS), including fluid nitrogen and fluid Fe N alloy. On the basis of these observations, we propose a nitrogen driven mechanism for LLPS in borate melts. Specifically, nitrogen reduces metal cations in the borate network, altering their coordination environments and triggering a substantial reorganization of the melt structure. This coordination induced restructuring destabilizes the borate melt and promotes the LLPS of fluid Fe N alloy. Our in-situ observations suggest a general pathway for high pressure metal oxide to nitride conversion. This study provides a direct visualization of a pres-sure-enabled chemical reaction that is inaccessible under ambient conditions, offering fundamental in-sight into how high pressure reshapes chemical reaction pathways and enables the synthesis of metal nitrides. △ Less

Submitted 8 August, 2026; v1 submitted 29 September, 2025; originally announced September 2025.

Comments: 13 pages, 5 figures

Ion-neutral chemistry at icy moons: the case of Ganymede

Authors: A. Beth, M. Galand, X. Jia, F. Leblanc

Abstract: Icy moons orbiting giant planets are often described as airless bodies though they host an exosphere where collisions between neutral species are scarce. In the case of Ganymede, the neutral composition is dominated by $\mathrm{H_2O}$, $\mathrm{H_2}$, and $\mathrm{O_2}$. Past observations by Galileo showed that Ganymede hosts an ionosphere and those by Juno revealed the presence of… ▽ More Icy moons orbiting giant planets are often described as airless bodies though they host an exosphere where collisions between neutral species are scarce. In the case of Ganymede, the neutral composition is dominated by $\mathrm{H_2O}$, $\mathrm{H_2}$, and $\mathrm{O_2}$. Past observations by Galileo showed that Ganymede hosts an ionosphere and those by Juno revealed the presence of $\mathrm{H_3^+}$, an ion species only stemming from ion-neutral collisions. $\mathrm{H_3^+}$ detection suggests that ions and neutrals might still collide and be the source of new ion species on icy moons. We examine Ganymede's ability to host a more diverse ionosphere in terms of ion composition than previously thought and predict its variety. We upgraded our test-particle code of Ganymede's ionosphere, formerly collisionless, to include ion-neutral collisions in a probabilistic manner. The updated code is applied to three Galileo flybys of Ganymede that were investigated in the absence of chemistry. Both sets of simulations have been compared and the effect of ion-neutral chemistry has been assessed. We show that in the case of an exosphere predominantly composed of $\mathrm{H_2O}$, $\mathrm{H_2}$, and $\mathrm{O_2}$, the ionosphere is made not only of their associated cations but also of $\mathrm{H_3^+}$, $\mathrm{H_3O^+}$, and $\mathrm{O_2H^+}$. Simulations reveal that, depending on the location, the contribution of $\mathrm{H_3^+}$ and $\mathrm{H_3O^+}$ to the ion composition may be significant. Strong dayside/nightside and Jovian/anti-Jovian asymmetries in the ion composition are identified. Our findings are key to interpreting Juno and future JUICE ion mass spectrometer datasets. △ Less

Submitted 26 September, 2025; originally announced September 2025.

Comments: accepted for publication in MNRAS

Journal ref: Mon Not R Astron Soc (2025) 95-112

A data-driven global ocean forecasting model with sub-daily and eddy-resolving resolution

Authors: Yuan Niu, Qiusheng Huang, Xiaohui Zhong, Anboyu Guo, Lei Chen, Xiaoyan Jia, Jiawei Qi, Dianjun Zhang, Hao Li, Xuefeng Zhang

Abstract: High-fidelity ocean forecasting at high spatial and temporal resolution is essential for capturing fine-scale dynamical features, with profound implications for hazard prediction, maritime navigation, and sustainable ocean management. While conventional numerical models can generate sub-daily, eddy-resolving forecasts, they demand substantial computational resources and often struggle to maintain… ▽ More High-fidelity ocean forecasting at high spatial and temporal resolution is essential for capturing fine-scale dynamical features, with profound implications for hazard prediction, maritime navigation, and sustainable ocean management. While conventional numerical models can generate sub-daily, eddy-resolving forecasts, they demand substantial computational resources and often struggle to maintain predictive skill at such fine scales. Data-driven models offer a promising alternative with significantly higher computational efficiency; however, most are constrained to daily outputs and show a rapid decay in accuracy when extended to sub-daily timescales. Here, we introduce TianHai, the first-of-its-kind global data-driven 6-hour forecasting model, which delivers predictions at 1/12° eddy-resolving resolution with a vertical extent down to 1,500 m. A key feature of TianHai is the integration of atmospheric forcings through FuXi-Atmosphere, a data-driven atmospheric forecasting system, which enables the explicit representation of air-sea coupling effects. Unlike conventional approaches, TianHai does not rely on numerical atmospheric models or external meteorological forecasts, making it a fully data-driven framework for coupled prediction. Benchmark experiments demonstrate that TianHai delivers state-of-the-art performance in forecasting temperature and salinity profiles, zonal and meridional currents, sea surface temperature, and sea level anomalies for lead times ranging from 1 to 10 days. △ Less

Submitted 26 September, 2025; v1 submitted 21 September, 2025; originally announced September 2025.

Comments: Due to numerous errors and academic issues in the article that cannot be fixed in a short period of time, and to avoid causing confusion for other researchers, we have decided to retract the paper first. We will consider resubmitting it after making improvements in the future

Thermal Cycling Reliability of Hybrid Pixel Sensor Modules for The ATLAS High Granularity Timing Detector

Authors: Y. Li, A. Aboulhorma, M. Ait Tamlihat, H. M. Alfanda, N. Atanov, O. Atanova, I. Azzouzi, J. Barreiro Guimarães Da Costa, T. Beau, D. Benchekroun, F. Bendebba, Y. Bimgdi, A. Blot, A. Boikov, J. Bonis, D. Boumediene, C. Brito, A. S. Brogna, A. M. Burger, L. Cadamuro, Y. Cai, N. Cartalade, R. Casanova Mohr, Y. Che, X. Chen , et al. (203 additional authors not shown)

Abstract: The reliability of bump connection structures has become a critical aspect of future silicon detectors for particle physics. The High Granularity Timing Detector (HGTD) for the ATLAS experiment at the High-Luminosity Large Hadron Collider will require 8032 hybrid pixel sensor modules, composed of two Low Gain Avalanche Diode sensors bump-bonded to two readout ASICs and glued to a passive PCB. The… ▽ More The reliability of bump connection structures has become a critical aspect of future silicon detectors for particle physics. The High Granularity Timing Detector (HGTD) for the ATLAS experiment at the High-Luminosity Large Hadron Collider will require 8032 hybrid pixel sensor modules, composed of two Low Gain Avalanche Diode sensors bump-bonded to two readout ASICs and glued to a passive PCB. The detector will operate at low temperature (-30 degrees Celsius) to mitigate the impact of irradiation. The thermomechanical reliability of flip-chip bump connections in HGTD modules is a critical concern, particularly due to their characteristically lower bump density (pixel pitch dimensions of 1.3 mm by 1.3 mm). This paper elaborates on the challenges arising from this design characteristic. Finite element analysis and experimental testing were employed to investigate failure modes in the flip-chip bump structures under thermal cycling from -45 degrees Celsius to 40 degrees Celsius and to guide the module redesign. The optimized design demonstrates significantly enhanced robustness and is projected to fulfill the full lifetime requirements of the HGTD. △ Less

Submitted 17 September, 2025; originally announced September 2025.

Comments: 15 pages, 12 figures, 7 tables

Electrically pumped ultra-efficient quantum frequency conversion on thin film lithium niobate chip

Authors: Xina Wang, Xu-Feng Jiao, Bo Cao, Yang Liu, Xiu-Ping Xie, Ming-Yang Zheng, Qiang Zhang, Jian-Wei Pan

Abstract: Quantum frequency conversion (QFC) plays a crucial role in constructing seamless interconnection between quantum systems operating at different wavelengths. To advance future quantum technology, chip-scale integrated QFC components, featuring high efficiency, small footprint, low power consumption and high scalability, are indispensable. In this work, we demonstrate the first hybrid integrated QFC… ▽ More Quantum frequency conversion (QFC) plays a crucial role in constructing seamless interconnection between quantum systems operating at different wavelengths. To advance future quantum technology, chip-scale integrated QFC components, featuring high efficiency, small footprint, low power consumption and high scalability, are indispensable. In this work, we demonstrate the first hybrid integrated QFC chip on thin film lithium niobate platform that connects the telecom and visible bands. Benefiting from the periodically poled microring resonator with ulta-high normalized conversion efficiency of 386,000 %/W, an ultra-low pump power of 360 μW is achieved which is more than two orders of magnitude lower than traditional straight waveguide scheme. By injecting current into the chip, an on-chip quantum efficiency of 57% and a noise count of ~ 7k counts per second are achieved. Such an electrically pumped, integrated and scalable QFC chip would significantly advancing the integration of quantum network and the development of chip-scale quantum optical systems. △ Less

Submitted 4 September, 2025; originally announced September 2025.

Comments: 20 pages, 7 figures

A high-lying isomer in ^{92}Zr with lifetime modulated by the atomic charge states: a proposed approach for a nuclear gamma-ray laser

Authors: C. X. Jia, S. Guo, B. Ding, X. H. Zhou, C. X. Yuan, W. Hua J. G. Wang, S. W. Xu, C. M. Petrache, E. A. Lawrie, Y. B. Wu, Y. D. Fang, Y. H. Qiang, Y. Y. Yang, J. B. Ma, J. L. Chen, H. X. Chen, F. Fang, Y. H. Yu, B. F. Lv, F. F. Zeng, Q. B. Zeng, H. Huang, Z. H. Jia, W. Liang, W. Q. Zhang , et al. (23 additional authors not shown)

Abstract: The nuclides ^{92}Zr are produced and transported by using a radioactive beam line to a lowbackground detection station. After a flight time of about 1.14 μs, the ions are implanted into a carbon foil, and four γ rays deexciting the 8+ state in ^{92}Zr are observed in coincidence with the implantation signals within a few nanoseconds. We conjecture that there exists an isomer located slightly abov… ▽ More The nuclides ^{92}Zr are produced and transported by using a radioactive beam line to a lowbackground detection station. After a flight time of about 1.14 μs, the ions are implanted into a carbon foil, and four γ rays deexciting the 8+ state in ^{92}Zr are observed in coincidence with the implantation signals within a few nanoseconds. We conjecture that there exists an isomer located slightly above the 8^{+} state in ^{92}Zr. The isomeric lifetime in highly charged states is extended significantly due to the blocking of internal conversion decay channels, enabling its survival over the transportation. During the slowing-down process in the carbon foil, the ^{92}Zr ions capture electron and evolve toward neutral atoms, and consequently the lifetime is restored to a normal short value. Such a high-lying isomer depopulated by a low-energy transition may provide unique opportunity to develop nuclear γ laser. △ Less

Submitted 3 September, 2025; originally announced September 2025.

Electro-optic frequency comb-empowered precise measurement of the dynamic frequency of a laser

Authors: Weiwei Yang, Xingyu Jia, Jingyi Wang, Xinlun Cai, Yang Li, Guanhao Wu

Abstract: Frequency-modulated lasers (FMLs) are widely used in spectroscopy, biology, and LiDAR. The performance of these applications highly depends on the fast and precise tracking of the FMLs' absolute frequency, which remains a challenge. Here we demonstrate integrated lithium niobate electro-optic frequency combs with arbitrarily tunable repetition rates and a 29.45-nm bandwidth, enabling precise track… ▽ More Frequency-modulated lasers (FMLs) are widely used in spectroscopy, biology, and LiDAR. The performance of these applications highly depends on the fast and precise tracking of the FMLs' absolute frequency, which remains a challenge. Here we demonstrate integrated lithium niobate electro-optic frequency combs with arbitrarily tunable repetition rates and a 29.45-nm bandwidth, enabling precise tracking of the absolute frequency of an FML with a chirp rate as high as $2\times10^{18}\,\mathrm{Hz/s}$, which is over three orders of magnitude above the state of the art. This method enables frequency-modulated continuous-wave ranging using an FML with severe mode hops, unlocking great potential for improving the ranging resolution and acquisition rate. Our method lays the foundation for FML-based high-precision measurements of frequency, distance, and time, leading to profound implications in fundamental science and engineering applications. △ Less

Submitted 22 August, 2025; originally announced August 2025.

Electrically pumped ultrabright entangled photons on chip

Authors: Xu-Feng Jiao, Ming-Yang Zheng, Yi-Hang Chen, Bo Cao, Xina Wang, Yang Liu, Cheng-Ao Yang, Xiu-Ping Xie, Chao-Yang Lu, Zhi-Chuan Niu, Qiang Zhang, Jian-Wei Pan

Abstract: Entangled photon sources (EPS) are essential for quantum science and technology. Despite advancements in integrated optical platforms like thin-film lithium niobate, a scalable, high-performance, chip-scale EPS has remained elusive. We address this by demonstrating an electrically pumped, post-selection-free polarization-EPS, achieved through hybrid integration of a distributed feedback laser with… ▽ More Entangled photon sources (EPS) are essential for quantum science and technology. Despite advancements in integrated optical platforms like thin-film lithium niobate, a scalable, high-performance, chip-scale EPS has remained elusive. We address this by demonstrating an electrically pumped, post-selection-free polarization-EPS, achieved through hybrid integration of a distributed feedback laser with thin-film lithium niobate chip which integrates periodically poled lithium niobate waveguides, beam splitter, and polarization rotator combiner. By injecting current into the chip, we realize a high-performance EPS with a bandwidth of 73 nm and an entanglement pair generation rate of 4.5*10^10 pairs/s/mW. The polarization entanglement shows Bell-state fidelities above 96% across frequency-correlated modes. This compact, integrated EPS enables key applications, including high-speed quantum key distribution via wavelength division multiplexing, satellite-based quantum communication, and entanglement-based quantum metrology. △ Less

Submitted 20 August, 2025; originally announced August 2025.

Comments: 21 pages, 8 figures

New Insights into Automatic Treatment Planning for Cancer Radiotherapy Using Explainable Artificial Intelligence

Authors: Md Mainul Abrar, Xun Jia, Yujie Chi

Abstract: Objective: This study aims to uncover the opaque decision-making process of an artificial intelligence (AI) agent for automatic treatment planning. Approach: We examined a previously developed AI agent based on the Actor-Critic with Experience Replay (ACER) network, which automatically tunes treatment planning parameters (TPPs) for inverse planning in prostate cancer intensity modulated radiothe… ▽ More Objective: This study aims to uncover the opaque decision-making process of an artificial intelligence (AI) agent for automatic treatment planning. Approach: We examined a previously developed AI agent based on the Actor-Critic with Experience Replay (ACER) network, which automatically tunes treatment planning parameters (TPPs) for inverse planning in prostate cancer intensity modulated radiotherapy. We selected multiple checkpoint ACER agents from different stages of training and applied an explainable AI (EXAI) method to analyze the attribution from dose-volume histogram (DVH) inputs to TPP-tuning decisions. We then assessed each agent's planning efficacy and efficiency and evaluated their policy and final TPP tuning spaces. Combining these analyses, we systematically examined how ACER agents generated high-quality treatment plans in response to different DVH inputs. Results: Attribution analysis revealed that ACER agents progressively learned to identify dose-violation regions from DVH inputs and promote appropriate TPP-tuning actions to mitigate them. Organ-wise similarities between DVH attributions and dose-violation reductions ranged from 0.25 to 0.5 across tested agents. Agents with stronger attribution-violation similarity required fewer tuning steps (~12-13 vs. 22), exhibited a more concentrated TPP-tuning space with lower entropy (~0.3 vs. 0.6), converged on adjusting only a few TPPs, and showed smaller discrepancies between practical and theoretical tuning steps. Putting together, these findings indicate that high-performing ACER agents can effectively identify dose violations from DVH inputs and employ a global tuning strategy to achieve high-quality treatment planning, much like skilled human planners. Significance: Better interpretability of the agent's decision-making process may enhance clinician trust and inspire new strategies for automatic treatment planning. △ Less

Submitted 19 August, 2025; originally announced August 2025.

Comments: 19 pages, 7 figures, 1 table, Oral presentation at the conference 'American Association of Physicists in Medicine 2025, 67th Annual Meeting and Exhibition'

MSC Class: Primary 68T27; Secondary 68T99; ACM Class: J.3; I.2.m

R-Index: A Robust Metric for IVIM Parameter Estimation on Clinical MRI Scanners

Authors: Yan Dai, Xun Jia, Yen-peng Liao, Jie Deng

Abstract: Background: Intravoxel Incoherent Motion (IVIM) model characterizes both water diffusion and perfusion in tissues, providing quantitative biomarkers valuable for tumor tissue characterization. However, parameter estimation based on this model is challenging due to its ill-posed nature, resulting in poor reproducibility, particularly at low signal to noise ratios (SNRs) in a clinic scenario. Purpos… ▽ More Background: Intravoxel Incoherent Motion (IVIM) model characterizes both water diffusion and perfusion in tissues, providing quantitative biomarkers valuable for tumor tissue characterization. However, parameter estimation based on this model is challenging due to its ill-posed nature, resulting in poor reproducibility, particularly at low signal to noise ratios (SNRs) in a clinic scenario. Purpose: This study analyzes the uncertainty of IVIM model fitting, quantifies parameter collinearity, and introduces a new index with enhanced robustness to enhance clinical applicability of the IVIM model. Study Type: Prospective. Population: One healthy volunteer. Field Strength/Sequence: 1.5T; single-shot EPI DWI. Assessment: The probability distributions of estimated IVIM parameters were evaluated across a clinically relevant range. Collinearity among parameters was assessed and a new metric, the R-index, was proposed. The R-index linearly combines individual IVIM parameters to mitigate collinearity and reduce estimation uncertainty. Simulation and a volunteer study was conducted to validate the presence of parameter collinearity and to assess the robustness of the R-index. Statistical Tests: N/A Results: In simulation studies with a typical clinical setting (SNR = 20), normalized IVIM parameters exhibited mean standard deviations ranging from 0.107 to 0.269, while the R-index showed a reduced deviation of 0.064. Repeated scans in a healthy volunteer confirmed the presence of parameter collinearity, with 32% of voxels exhibiting statistically significant correlations (p < 0.05) among fitted IVIM parameters, and a mean Pearson correlation coefficient of r = -0.96. Data Conclusion: The R-index provides a robust metric for IVIM model fitting under low SNR conditions typical of clinical MRI, offering improved reproducibility and potential for broader clinical applicability. △ Less

Submitted 1 August, 2025; originally announced August 2025.

Noise Filtering Algorithm Based on Graph Neural Network for STCF Drift Chamber

Authors: Xiaoqian Jia, Xiaoshuai Qin, Teng Li, Xueyao Zhang, Xiaoqian Hu, Shuangbing Song, Hang Zhou, Xiaocong Ai, Jin Zhang, Xingtao Huang

Abstract: The super $τ$-charm facility (STCF) is a next-generation electron-positron collider with high luminosity proposed in China. The higher luminosity leads to increased background level, posing significant challenges for track reconstruction of charged particles. Particularly in the low transverse momentum region, the current track reconstruction algorithm is notably affected by background, resulting… ▽ More The super $τ$-charm facility (STCF) is a next-generation electron-positron collider with high luminosity proposed in China. The higher luminosity leads to increased background level, posing significant challenges for track reconstruction of charged particles. Particularly in the low transverse momentum region, the current track reconstruction algorithm is notably affected by background, resulting in suboptimal reconstruction efficiency and a high fake rate. To address this challenge, we propose a Graph Neural Network (GNN)-based noise filtering algorithm (GNF Algorithm) as a preprocessing step for the track reconstruction. The GNF Algorithm introduces a novel method to convert detector data into graphs and applies a tiered threshold strategy to map GNN-based edge classification results onto signal-noise separation. The study based on Monte Carlo (MC) data shows that with the implementation of the GNF Algorithm, the reconstruction efficiency with the standard background is comparable to the case without background, while the fake rate is significantly reduced. Thus, GNF Algorithm provides essential support for the STCF tracking software. △ Less

Submitted 12 July, 2025; originally announced July 2025.

Comments: 11 pages, 10 figures

Accelerating Correlated Wave Function Calculations with Hierarchical Matrix Compression of the Two-Electron Integrals

Authors: Hongji Gao, Xiangmin Jiao, Benjamin G. Levine

Abstract: Leveraging matrix sparsity has proven a fruitful strategy for accelerating quantum chemical calculations. Here we present the hierarchical SOS-MP2 algorithm, which uses hierarchical matrix ($\mathcal{H}^{2}$) compression of the electron repulsion integral (ERI) tensor to reduce both time and space complexity. This approach is based on the atomic orbital Laplace transform MP2 calculations, leveragi… ▽ More Leveraging matrix sparsity has proven a fruitful strategy for accelerating quantum chemical calculations. Here we present the hierarchical SOS-MP2 algorithm, which uses hierarchical matrix ($\mathcal{H}^{2}$) compression of the electron repulsion integral (ERI) tensor to reduce both time and space complexity. This approach is based on the atomic orbital Laplace transform MP2 calculations, leveraging the data sparsity of the ERI tensor and the element-wise sparsity of the energy-weighted density matrices. The $\mathcal{H}^{2}$ representation approximates the ERI tensor in a block low-rank form, taking advantage of the inherent low-rank nature of the repulsion integrals between distant sets of atoms. The resulting algorithm enables the calculation of the Coulomb-like term of the MP2 energy with a theoretical time complexity of $\mathcal{O}(N^{2}\log N)$ and a space complexity of $\mathcal{O}(N^{2}\log N)$, where $N$ denotes the number of basis functions. Numerical tests show asymptotic time and space complexities better than $\mathcal{O}(N^{2})$ for both linear alkanes and three-dimensional water clusters. △ Less

Submitted 19 June, 2025; originally announced June 2025.

Nuclear Physics at BRIF

Authors: Wei Nan, Bing Guo, Jie Chen, Baoqun Cui, Wei Fu, Xianlu Jia, Chaoxin Kan, Jiayinghao Li, Yunju Li, Chengjian Lin, Yihui Liu, Nanru Ma, Zhaohua Peng, Yangping Shen, Guofang Song, Jun Su, Bing Tang, Haorui Wang, Youbao Wang, Lei Yang, Xiaofei Yang, Zhiguo Yin, Yun Zheng, Tianjue Zhang, Weiping Liu

Abstract: The Beijing Radioactive Ion-beam Facility (BRIF), which is based on Isotope Separation On-Line (ISOL) technique, consists of a 100 MeV proton cyclotron as the driving accelerator, a two-stage ISOL system for ion separation, a 13-MV tandem accelerator for post-acceleration, a superconducting linac for further boosting beam energies. It is capable of providing ISOL beams in the energy range from 60… ▽ More The Beijing Radioactive Ion-beam Facility (BRIF), which is based on Isotope Separation On-Line (ISOL) technique, consists of a 100 MeV proton cyclotron as the driving accelerator, a two-stage ISOL system for ion separation, a 13-MV tandem accelerator for post-acceleration, a superconducting linac for further boosting beam energies. It is capable of providing ISOL beams in the energy range from 60 to 300 keV, and post-accelerated beams in the energy range from 3 to 10 MeV/u for nuclei with mass numbers of A < 80 by Isotope Separation On-Line (ISOL) technique. For nuclei with A up to 170, energies are still able to reach 3 MeV/u. This facility offers opportunities to address key questions of current interest in nuclear astrophysics, nuclear structure and reactions of unstable nuclei. In this review we present a comprehensive introduction to the BRIF and the typical experimental instruments installed on it, and then summarize current experimental results on unstable Na and Rb isotopes and future plan for further development of the BRIF to improve its performance. △ Less

Submitted 27 June, 2025; v1 submitted 16 March, 2025; originally announced March 2025.

Comments: 82 pages, 77 figures

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.

Ionosphere of Ganymede: Galileo observations versus test particle simulation

Authors: Arnaud Beth, Marina Galand, Ronan Modolo, Xianzhe Jia, François Leblanc, Hans Huybrighs

Abstract: In this paper, we model the plasma environment of Ganymede by means of a collisionless test particle simulation. By coupling the outputs from a DSMC simulation of Ganymede's exosphere (i.e. number density profiles of neutral species such as $\mathrm{H}$, $\mathrm{H_2}$, $\mathrm{O}$, $\mathrm{HO}$, $\mathrm{H_2O}$, $\mathrm{O_2}$ for which we provide parametrisation) with those of a MagnetoHydroDy… ▽ More In this paper, we model the plasma environment of Ganymede by means of a collisionless test particle simulation. By coupling the outputs from a DSMC simulation of Ganymede's exosphere (i.e. number density profiles of neutral species such as $\mathrm{H}$, $\mathrm{H_2}$, $\mathrm{O}$, $\mathrm{HO}$, $\mathrm{H_2O}$, $\mathrm{O_2}$ for which we provide parametrisation) with those of a MagnetoHydroDynamic simulation of the interaction between Ganymede and the Jovian plasma (i.e. electric and magnetic fields), we perform a comparison between simulated ion plasma densities and ion energy spectra with those observed in-situ during 6 close flybys of Ganymede by the Galileo spacecraft. We find that not only our test particle simulation sometimes can well reproduce the in-situ ion number density measurement, but also the dominant ion species during these flybys are $\mathrm{H_2^+}$, $\mathrm{O_2^+}$, and occasionally $\mathrm{H_2O^+}$. Although the observed ion energy spectra cannot be reproduced exactly, the simulated ion energy spectra exhibit similar trends to those observed near the closest approach and near the magnetopause crossings but at lower energies. We show that the neutral exosphere plays an important role in supplying plasma to Ganymede's magnetised environment and that additional mechanisms may be at play to energise/accelerate newborn ions from the neutral exosphere. △ Less

Submitted 18 February, 2025; originally announced February 2025.

Comments: accepted for publication in MNRAS

A Kernel Ridge Regression Combining Nonlinear ROMs for Accurate Flow Field Reconstruction with Discontinuities

Authors: Weiji Wang, Chunlin Gong, Xuyi Jia, Chunna Li

Abstract: Nonlinear reduced-order models (ROMs), represented by manifold learning (ML), can effectively improve the modeling accuracy of nonlinear flow fields with discontinuities. However, the inverse mapping from low-dimensional manifold coordinates to high-dimensional flow fields often introduces considerable reconstruction errors, leading to inaccuracy in the locations of discontinuities. To address thi… ▽ More Nonlinear reduced-order models (ROMs), represented by manifold learning (ML), can effectively improve the modeling accuracy of nonlinear flow fields with discontinuities. However, the inverse mapping from low-dimensional manifold coordinates to high-dimensional flow fields often introduces considerable reconstruction errors, leading to inaccuracy in the locations of discontinuities. To address this challenge, a novel reconstruction method is proposed to enhance the accuracy of reconstructing flow fields with discontinuities. The method employs kernel ridge regression (KRR) to construct a set of nonlinear modes rich in discontinuity information, sequentially these modes are nonlinearly combined with manifold coordinates to achieve accurate flow field reconstruction. The proposed reconstruction method is validated to reconstruct the transonic flow fields over RAE2822 airfoil. Comparison results demonstrate that the method achieves superior reconstruction accuracy compared to existing approaches, especially in reconstructing flow fields' discontinuous regions and precisely capturing discontinuities. This work provides an effective and highly interpretable solution for improving the accuracy of nonlinear ROMs in discontinuous flow fields modeling. △ Less

Submitted 16 February, 2025; originally announced February 2025.

Detection and characterization of targets in complex media using fingerprint matrices

Authors: Arthur Le Ber, Antton Goïcoechea, Lukas M. Rachbauer, William Lambert, Xiaoping Jia, Mathias Fink, Arnaud Tourin, Stefan Rotter, Alexandre Aubry

Abstract: When waves propagate through a complex medium, they undergo several scattering events. This phenomenon is detrimental to imaging, as it causes full blurring of the image. Here we describe a method for detecting, localizing and characterizing any scattering target embedded in a complex medium. We introduce a fingerprint operator that contains the specific signature of the target with respect to its… ▽ More When waves propagate through a complex medium, they undergo several scattering events. This phenomenon is detrimental to imaging, as it causes full blurring of the image. Here we describe a method for detecting, localizing and characterizing any scattering target embedded in a complex medium. We introduce a fingerprint operator that contains the specific signature of the target with respect to its environment. When applied to the recorded reflection matrix, it provides a likelihood index of the target state. This state can be the position of the target for localization purposes, its shape for characterization or any other parameter that influences its response. We demonstrate the versatility of our method by performing proof-of-concept ultrasound experiments on elastic spheres buried inside a strongly scattering granular suspension and on lesion markers, which are commonly used to monitor breast tumours, embedded in a foam mimicking soft tissue. Furthermore, we show how the fingerprint operator can be leveraged to characterize the complex medium itself by mapping the fibre architecture within muscle tissue. Our method is broadly applicable to different types of waves beyond ultrasound for which multi-element technology allows a reflection matrix to be measured. △ Less

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

Comments: 49 pages, 20 figures, 2 tables

Journal ref: Nature Physics, 2025

Actor Critic with Experience Replay-based automatic treatment planning for prostate cancer intensity modulated radiotherapy

Authors: Md Mainul Abrar, Parvat Sapkota, Damon Sprouts, Xun Jia, Yujie Chi

Abstract: Background: Real-time treatment planning in IMRT is challenging due to complex beam interactions. AI has improved automation, but existing models require large, high-quality datasets and lack universal applicability. Deep reinforcement learning (DRL) offers a promising alternative by mimicking human trial-and-error planning. Purpose: Develop a stochastic policy-based DRL agent for automatic trea… ▽ More Background: Real-time treatment planning in IMRT is challenging due to complex beam interactions. AI has improved automation, but existing models require large, high-quality datasets and lack universal applicability. Deep reinforcement learning (DRL) offers a promising alternative by mimicking human trial-and-error planning. Purpose: Develop a stochastic policy-based DRL agent for automatic treatment planning with efficient training, broad applicability, and robustness against adversarial attacks using Fast Gradient Sign Method (FGSM). Methods: Using the Actor-Critic with Experience Replay (ACER) architecture, the agent tunes treatment planning parameters (TPPs) in inverse planning. Training is based on prostate cancer IMRT cases, using dose-volume histograms (DVHs) as input. The model is trained on a single patient case, validated on two independent cases, and tested on 300+ plans across three datasets. Plan quality is assessed using ProKnow scores, and robustness is tested against adversarial attacks. Results: Despite training on a single case, the model generalizes well. Before ACER-based planning, the mean plan score was 6.20$\pm$1.84; after, 93.09% of cases achieved a perfect score of 9, with a mean of 8.93$\pm$0.27. The agent effectively prioritizes optimal TPP tuning and remains robust against adversarial attacks. Conclusions: The ACER-based DRL agent enables efficient, high-quality treatment planning in prostate cancer IMRT, demonstrating strong generalizability and robustness. △ Less

Submitted 1 February, 2025; originally announced February 2025.

Comments: 27 Pages, 8 Figures, 4 Tables

MSC Class: 92C50 (Primary) 68T07 (Secondary) ACM Class: I.2.1; J.2; J.3

Universal Catalyst Design Framework for Electrochemical Hydrogen Peroxide Synthesis Facilitated by Local Atomic Environment Descriptors

Authors: Zhijian Liu, Yan Liu, Bingqian Zhang, Yuqi Zhang, Tianxiang Gao, Mingzhe Li, Xue Jia, Di Zhang, Heng Liu, Xuqiang Shao, Li Wei, Hao Li, Weijie Yang

Abstract: Developing a universal and precise design framework is crucial to search high-performance catalysts, but it remains a giant challenge due to the diverse structures and sites across various types of catalysts. To address this challenge, herein, we developed a novel framework by the refined local atomic environment descriptors (i.e., weighted Atomic Center Symmetry Function, wACSF) combined with mac… ▽ More Developing a universal and precise design framework is crucial to search high-performance catalysts, but it remains a giant challenge due to the diverse structures and sites across various types of catalysts. To address this challenge, herein, we developed a novel framework by the refined local atomic environment descriptors (i.e., weighted Atomic Center Symmetry Function, wACSF) combined with machine learning (ML), microkinetic modeling, and computational high-throughput screening. This framework is successfully integrated into the Digital Catalysis Database (DigCat), enabling efficient screening for 2e- water oxidation reaction (2e- WOR) catalysts across four material categories (i.e., metal alloys, metal oxides and perovskites, and single-atom catalysts) within a ML model. The proposed wACSF descriptors integrating both geometric and chemical features are proven effective in predicting the adsorption free energies with ML. Excitingly, based on the wACSF descriptors, the ML models accurately predict the adsorption free energies of hydroxyl (ΔGOH*) and oxygen (ΔGO*) for such a wide range of catalysts, achieving R2 values of 0.84 and 0.91, respectively. Through density functional theory calculations and microkinetic modeling, a universal 2e- WOR microkinetic volcano model was derived with excellent agreement with experimental observations reported to date, which was further used to rapidly screen high-performance catalysts with the input of ML-predicted ΔGOH*. Most importantly, this universal framework can significantly improve the efficiency of catalyst design by considering multiple types of materials at the same time, which can dramatically accelerate the screening of high-performance catalysts. △ Less

Submitted 22 January, 2025; originally announced January 2025.

Comments: 33

Modeling Continuous Spatial-temporal Dynamics of Turbulent Flow with Test-time Refinement

Authors: Shengyu Chen, Peyman Givi, Can Zheng, Xiaowei Jia

Abstract: The precise simulation of turbulent flows holds immense significance across various scientific and engineering domains, including climate science, freshwater science, and energy-efficient manufacturing. Within the realm of simulating turbulent flows, large eddy simulation (LES) has emerged as a prevalent alternative to direct numerical simulation (DNS), offering computational efficiency. However,… ▽ More The precise simulation of turbulent flows holds immense significance across various scientific and engineering domains, including climate science, freshwater science, and energy-efficient manufacturing. Within the realm of simulating turbulent flows, large eddy simulation (LES) has emerged as a prevalent alternative to direct numerical simulation (DNS), offering computational efficiency. However, LES cannot accurately capture the full spectrum of turbulent transport scales and is present only at a lower spatial resolution. Reconstructing high-fidelity DNS data from the lower-resolution LES data is essential for numerous applications, but it poses significant challenges to existing super-resolution techniques, primarily due to the complex spatio-temporal nature of turbulent flows. This paper proposes a novel flow reconstruction approach that leverages physical knowledge to model flow dynamics. Different from traditional super-resolution techniques, the proposed approach uses LES data only in the testing phase through a degradation-based refinement approach to enforce physical constraints and mitigate cumulative reconstruction errors over time. Furthermore, a feature sampling strategy is developed to enable flow data reconstruction across different resolutions. The results on two distinct sets of turbulent flow data indicate the effectiveness of the proposed method in reconstructing high-resolution DNS data, preserving the inherent physical attributes of flow transport, and achieving DNS reconstruction at different resolutions. △ Less

Submitted 27 December, 2024; originally announced December 2024.

Comments: 14 pages

Physics-Guided Fair Graph Sampling for Water Temperature Prediction in River Networks

Authors: Erhu He, Declan Kutscher, Yiqun Xie, Jacob Zwart, Zhe Jiang, Huaxiu Yao, Xiaowei Jia

Abstract: This work introduces a novel graph neural networks (GNNs)-based method to predict stream water temperature and reduce model bias across locations of different income and education levels. Traditional physics-based models often have limited accuracy because they are necessarily approximations of reality. Recently, there has been an increasing interest of using GNNs in modeling complex water dynamic… ▽ More This work introduces a novel graph neural networks (GNNs)-based method to predict stream water temperature and reduce model bias across locations of different income and education levels. Traditional physics-based models often have limited accuracy because they are necessarily approximations of reality. Recently, there has been an increasing interest of using GNNs in modeling complex water dynamics in stream networks. Despite their promise in improving the accuracy, GNNs can bring additional model bias through the aggregation process, where node features are updated by aggregating neighboring nodes. The bias can be especially pronounced when nodes with similar sensitive attributes are frequently connected. We introduce a new method that leverages physical knowledge to represent the node influence in GNNs, and then utilizes physics-based influence to refine the selection and weights over the neighbors. The objective is to facilitate equitable treatment over different sensitive groups in the graph aggregation, which helps reduce spatial bias over locations, especially for those in underprivileged groups. The results on the Delaware River Basin demonstrate the effectiveness of the proposed method in preserving equitable performance across locations in different sensitive groups. △ Less

Submitted 21 December, 2024; originally announced December 2024.

Diff5T: Benchmarking Human Brain Diffusion MRI with an Extensive 5.0 Tesla K-Space and Spatial Dataset

Authors: Shanshan Wang, Shoujun Yu, Jian Cheng, Sen Jia, Changjun Tie, Jiayu Zhu, Haohao Peng, Yijing Dong, Jianzhong He, Fan Zhang, Yaowen Xing, Xiuqin Jia, Qi Yang, Qiyuan Tian, Hua Guo, Guobin Li, Hairong Zheng

Abstract: Diffusion magnetic resonance imaging (dMRI) provides critical insights into the microstructural and connectional organization of the human brain. However, the availability of high-field, open-access datasets that include raw k-space data for advanced research remains limited. To address this gap, we introduce Diff5T, a first comprehensive 5.0 Tesla diffusion MRI dataset focusing on the human brain… ▽ More Diffusion magnetic resonance imaging (dMRI) provides critical insights into the microstructural and connectional organization of the human brain. However, the availability of high-field, open-access datasets that include raw k-space data for advanced research remains limited. To address this gap, we introduce Diff5T, a first comprehensive 5.0 Tesla diffusion MRI dataset focusing on the human brain. This dataset includes raw k-space data and reconstructed diffusion images, acquired using a variety of imaging protocols. Diff5T is designed to support the development and benchmarking of innovative methods in artifact correction, image reconstruction, image preprocessing, diffusion modelling and tractography. The dataset features a wide range of diffusion parameters, including multiple b-values and gradient directions, allowing extensive research applications in studying human brain microstructure and connectivity. With its emphasis on open accessibility and detailed benchmarks, Diff5T serves as a valuable resource for advancing human brain mapping research using diffusion MRI, fostering reproducibility, and enabling collaboration across the neuroscience and medical imaging communities. △ Less

Submitted 9 December, 2024; originally announced December 2024.

Comments: 19 pages, 4 figures, 1 table

Scaling Particle Collision Data Analysis

Authors: Hengkui Wu, Panpan Chi, Yongfeng Zhu, Liujiang Liu, Shuyang Hu, Yuexin Wang, Chen Zhou, Qihao Wang, Yingsi Xin, Bruce Liu, Dahao Liang, Xinglong Jia, Manqi Ruan

Abstract: For decades, researchers have developed task-specific models to address scientific challenges across diverse disciplines. Recently, large language models (LLMs) have shown enormous capabilities in handling general tasks; however, these models encounter difficulties in addressing real-world scientific problems, particularly in domains involving large-scale numerical data analysis, such as experimen… ▽ More For decades, researchers have developed task-specific models to address scientific challenges across diverse disciplines. Recently, large language models (LLMs) have shown enormous capabilities in handling general tasks; however, these models encounter difficulties in addressing real-world scientific problems, particularly in domains involving large-scale numerical data analysis, such as experimental high energy physics. This limitation is primarily due to BPE tokenization's inefficacy with numerical data. In this paper, we propose a task-agnostic architecture, BBT-Neutron, which employs a binary tokenization method to facilitate pretraining on a mixture of textual and large-scale numerical experimental data. We demonstrate the application of BBT-Neutron to Jet Origin Identification (JoI), a critical categorization challenge in high-energy physics that distinguishes jets originating from various quarks or gluons. Our results indicate that BBT-Neutron achieves comparable performance to state-of-the-art task-specific JoI models. Furthermore, we examine the scaling behavior of BBT-Neutron's performance with increasing data volume, suggesting the potential for BBT-Neutron to serve as a foundational model for particle physics data analysis, with possible extensions to a broad spectrum of scientific computing applications for Big Science experiments, industrial manufacturing and spacial computing. The project code is available at https://github.com/supersymmetry-technologies/bbt-neutron. △ Less

Submitted 9 December, 2024; v1 submitted 28 November, 2024; originally announced December 2024.