Showing 1–50 of 190 results for author: Lai, Y

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup

Authors: Jayson R. Vavrek, Thomas D. MacDonald, Yue Shi Lai

Abstract: We present methods for recovering spectroscopic information from multiple concurrent photon interactions that would normally be lost due to pulse pileup. In particular, we focus on machine learning methods to recover information based on spatial (rather than temporal) energy deposition patterns in position-sensitive detectors. We construct two representative problems, namely (1) recovering the fra… ▽ More We present methods for recovering spectroscopic information from multiple concurrent photon interactions that would normally be lost due to pulse pileup. In particular, we focus on machine learning methods to recover information based on spatial (rather than temporal) energy deposition patterns in position-sensitive detectors. We construct two representative problems, namely (1) recovering the fraction of total energy deposition stemming from a monoenergetic signal vs. a smooth background; and (2) recovering the signal multiplicity, i.e., the number of interacting photons, in a pure-source-term example. In the signal fraction recovery problem, we use 3D convolutional neural networks (CNNs), fully-connected neural networks (FCNNs), a network based on the PointNet++ architecture, and two non-machine-learning methods to estimate the signal fraction in synthetic data when up to 20 total piled-up photons are present. The CNN, FCNN, and PointNet++ models reconstruct the signal energy deposition fractions with root mean square errors (RMSEs) of $14.5\%$, $18.8\%$, and $16.8\%$ given training datasets that fit in-core, while the classical methods perform poorly and will not improve with additional training data. In the multiplicity recovery problem, we demonstrate that, when trained with synthetically-piled-up real Cs-137 data, the 3D CNN architecture can recover the multiplicity with sub-photon RMSE, outperforming non-ML baselines. These methods can be adapted to future, more specific photon active interrogation applications, helping to re-enable spectroscopic analyses in those domains. △ Less

Submitted 10 August, 2026; originally announced August 2026.

Comments: 14 pages, 12 figures, 4 tables

Machine-Learning-Based Waveform Discrimination in the Front-End Electronics of the Belle II Central Drift Chamber for Cross-Talk Noise Reduction

Authors: Yun-Tsung Lai, Taichiro Koga, Yu Nakazawa, Nanae Taniguchi, Keisuke Yoshihara

Abstract: Machine learning (ML) inference on FPGAs has been widely adopted in real-time triggering of collider experiments for detector signature identification. In contrast, the ML application in Front-End Electronics (FEE) has not yet been fully explored, primarily due to constraints such as limited FPGA resources, power consumption, and localized detector coverage. In this work, we develop an ML-based wa… ▽ More Machine learning (ML) inference on FPGAs has been widely adopted in real-time triggering of collider experiments for detector signature identification. In contrast, the ML application in Front-End Electronics (FEE) has not yet been fully explored, primarily due to constraints such as limited FPGA resources, power consumption, and localized detector coverage. In this work, we develop an ML-based waveform discrimination method for the Central Drift Chamber (CDC) of the Belle II experiment to suppress cross-talk noise at the front-end level. The Belle II CDC is a key charged-particle tracking detector for both offline and the real-time hardware trigger. During Belle II operation, background wire hits have been observed in the CDC FEE, where multiple hits occur in neighboring anode wires by large energy deposit. The hardware track trigger employs a Hough transformation based on track segments formed by combining hits from multiple wire layers. Due to the reduced information, the track trigger is sensitive to cross-talk noise, hence resulting in an increased fake trigger rate with higher luminosity in the future. We employ compact and fast Boosted Decision Tree models implemented in a Xilinx Virtex-5 FPGA of the CDC FEE, where waveform is processed independently for each wire channel in a fully pipelined manner. Offline studies show that the cross-talk noise can be reduced by approximately a factor of two while maintaining a signal efficiency above 98%. The firmware validation during dedicated Belle II calibration runs demonstrated reductions of up to 50% in track segment and trigger rates while preserving the trigger acceptance for events containing tracks within 10%. This work demonstrates the technical feasibility of compact and low-latency ML inference in detector FEE and highlights its potential for future intelligent detector readout systems in high-energy physics experiments. △ Less

Submitted 27 July, 2026; originally announced July 2026.

Comments: 8 pages, 9 figures, submitted to IEEE Transaction on Nuclear Science

Robust class-gated single-pixel diffractive optical neural network with random-aberration-aware training

Authors: Xianjin Liu, Qiwen Bao, Ting Ma, Yihuan Liang, Yongqiu Lai, Bolun Zhang, Fansanqiu Li, Licheng Wang, Jun-Jun Xiao

Abstract: Optical computing offers the theoretical potential for high-speed, energy-efficient inference, yet its practical deployment remains constrained by fundamental input-output bottlenecks, particularly the reliance on electronic sensors with limited frame rates and stringent alignment requirements between optical components. Here, we demonstrate an image-class-gated single-pixel DONN that overcomes th… ▽ More Optical computing offers the theoretical potential for high-speed, energy-efficient inference, yet its practical deployment remains constrained by fundamental input-output bottlenecks, particularly the reliance on electronic sensors with limited frame rates and stringent alignment requirements between optical components. Here, we demonstrate an image-class-gated single-pixel DONN that overcomes these limitations by converting spatial complexity into a temporal intensity signature. Using a minimal architecture comprising a reconfigurable digital micromirror device and a single-pixel photodetector, we implement a virtual optical gate. The system time-multiplexes class-specific masks, causing the detector response to peak only when the mask index matches the input class. This allows the predicted label to be read out via peak timing rather than spatial localization, eliminating 2D sensor constraints. To bridge the persistent sim-to-real gap, we introduce a physics-aware training strategy using random-phase augmentation. This method renders the model intrinsically tolerant to phase aberrations and mechanical misalignments without requiring precise hardware modeling. Our prototype achieves 90.0%(MNIST) and 80.0% (Fashion-MNIST) accuracy at a readout rate of 5 kHz. By combining gigahertz-compatible single-pixel detection with robust and alignment-tolerant training, this work provides a scalable, hardware-efficient pathway toward real-time optical intelligent sensing. △ Less

Submitted 29 May, 2026; originally announced May 2026.

Comments: 5 figues

Generative AI impacts on intra-urban inequality and skill premium in Beijing

Authors: Xiliu He, Haoxiang Zhao, Mingyi Ma, Edward Wen Chuan Lai, Koei Enomoto, Anni Hu, Jiatong Li, Lingyun Chu, Yuan Lai

Abstract: Generative artificial intelligence (GenAI) is the first automation wave to reach high-cognitive tasks at scale, yet its effects on intra-urban inequality remain largely unknown. Using 5 million job postings from Beijing (2018--2024), we construct a neighborhood-level GenAI Exposure Index by aggregating task-level assessments from five leading large language models. We examine the spatial, structur… ▽ More Generative artificial intelligence (GenAI) is the first automation wave to reach high-cognitive tasks at scale, yet its effects on intra-urban inequality remain largely unknown. Using 5 million job postings from Beijing (2018--2024), we construct a neighborhood-level GenAI Exposure Index by aggregating task-level assessments from five leading large language models. We examine the spatial, structural and causal mechanisms of this shock. We find that GenAI exposure is highly concentrated in the city's core districts, deepening the intra-urban AI divide. Since 2023, high-exposure neighborhoods have experienced wage stagnation even as they continue to attract high-skilled workers -- a "high-skill trap." This wage penalty is driven by task de-skilling and intensified labor-market crowding. A difference-in-differences design centered on ChatGPT's release supports a causal interpretation. These findings challenge the prevailing theory of skill-biased technological change and provide a basis for inclusive AI governance in global technology hubs. △ Less

Submitted 25 May, 2026; originally announced May 2026.

Comments: 21 pages, 8 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

Can Transformers predict system collapse in dynamical systems?

Authors: Zheng-Meng Zhai, Celso Grebogi, Ying-Cheng Lai

Abstract: Transformer architectures have recently surged as promising solutions for nonlinear dynamical systems, proposed as foundation models capable of zero-shot dynamics reconstruction and forecasting. Despite this success, it remains unclear whether they can truly serve as reliable digital twins of dynamical systems, i.e., whether they capture the underlying physical dynamics in distinct parameter regim… ▽ More Transformer architectures have recently surged as promising solutions for nonlinear dynamical systems, proposed as foundation models capable of zero-shot dynamics reconstruction and forecasting. Despite this success, it remains unclear whether they can truly serve as reliable digital twins of dynamical systems, i.e., whether they capture the underlying physical dynamics in distinct parameter regimes, especially in parameter regimes from which no training data is taken. For parameter-space extrapolation in nonlinear dynamical systems, reservoir computing has demonstrated broad success, as proper training can turn it into an intrinsic dynamical system capable of capturing not only the dynamical climate of the target system but more importantly, how the climate changes with parameter. Transformers, in contrast, rely on permutation-invariant attention mechanisms that can limit their ability to capture how temporal structure changes with parameter. To determine if Transformers have the capability of dynamics extrapolation, we take predicting catastrophic collapse, which occurs when a bifurcation parameter crosses a critical threshold, as a benchmark task. Models are trained on trajectories in normal parameter regimes and then tested on parameters in an unseen regime with system collapse. Our results show that Transformers, across configurations, consistently fail to capture collapse, while reservoir computing reliably predicts the transitions. This surprising finding raises questions about the generalization ability of Transformers to dynamical systems, a topic warranting future research. △ Less

Submitted 5 May, 2026; originally announced May 2026.

Transferable Physics-Informed Representations via Closed-Form Head Adaptation

Authors: Jian Cheng Wong, Isaac Yin Chung Lai, Pao-Hsiung Chiu, Chin Chun Ooi, Abhishek Gupta, Yew-Soon Ong

Abstract: Physics-informed neural networks (PINNs) have garnered significant interest for their potential in solving partial differential equations (PDEs) that govern a wide range of physical phenomena. By incorporating physical laws into the learning process, PINN models have demonstrated the ability to learn physical outcomes reasonably well. However, current PINN approaches struggle to predict or solve n… ▽ More Physics-informed neural networks (PINNs) have garnered significant interest for their potential in solving partial differential equations (PDEs) that govern a wide range of physical phenomena. By incorporating physical laws into the learning process, PINN models have demonstrated the ability to learn physical outcomes reasonably well. However, current PINN approaches struggle to predict or solve new PDEs effectively when there is a lack of training examples, indicating they do not generalize well to unseen problem instances. In this paper, we present a transferable learning approach for PINNs premised on a fast Pseudoinverse PINN framework (Pi-PINN). Pi-PINN learns a transferable physics-informed representation in a shared embedding space and enables rapid solving of both known and unknown PDE instances via closed-form head adaptation using a least-squares-optimal pseudoinverse under PDE constraints. We further investigate the synergies between data-driven multi-task learning loss and physics-informed loss, providing insights into the design of more performant PINNs. We demonstrate the effectiveness of Pi-PINN on various PDE problems, including Poisson's equation, Helmholtz equation, and Burgers' equation, achieving fast and accurate physics-informed solutions without requiring any data for unseen instances. Pi-PINN can produce predictions 100-1000 times faster than a typical PINN, while producing predictions with 10-100 times lower relative error than a typical data-driven model even with only two training samples. Overall, our findings highlight the potential of transferable representations with closed-form head adaptation to enhance the efficiency and generalization of PINNs across PDE families and scientific and engineering applications. △ Less

Submitted 23 April, 2026; originally announced April 2026.

Comments: Accepted at IJCNN 2026

Vestibular reservoir computing

Authors: Smita Deb, Shirin Panahi, Mulugeta Haile, Ying-Cheng Lai

Abstract: Reservoir computing (RC) is a computational framework known for its training efficiency, making it ideal for physical hardware implementations. However, realizing the complex interconnectivity of traditional reservoirs in physical systems remains a significant challenge. This paper proposes a physical RC scheme inspired by the biological vestibular system. To overcome hardware complexity, we intro… ▽ More Reservoir computing (RC) is a computational framework known for its training efficiency, making it ideal for physical hardware implementations. However, realizing the complex interconnectivity of traditional reservoirs in physical systems remains a significant challenge. This paper proposes a physical RC scheme inspired by the biological vestibular system. To overcome hardware complexity, we introduce a designed uncoupled topology and demonstrate that it achieves performance comparable to fully coupled networks. We theoretically analyze the difference between these topologies by deriving a memory capacity formula for linear reservoirs, identifying specific conditions where both configurations yield equivalent memory. These analytical results are demonstrated to approximately hold for nonlinear reservoir systems. Furthermore, we systematically examine the impact of reservoir size on predictive statistics and memory capacity. Our findings suggest that uncoupled reservoir architectures offer a mathematically sound and practically feasible pathway for efficient physical reservoir computing. △ Less

Submitted 10 April, 2026; originally announced April 2026.

Comments: 24 pages, 11 figures

Anticipating tipping in spatiotemporal systems with machine learning

Authors: Smita Deb, Zheng-Meng Zhai, Mulugeta Haile, Ying-Cheng Lai

Abstract: In nonlinear dynamical systems, tipping refers to a critical transition from one steady state to another, typically catastrophic, steady state, often resulting from a saddle-node bifurcation. Recently, the machine-learning framework of parameter-adaptable reservoir computing has been applied to predict tipping in systems described by low-dimensional stochastic differential equations. However, anti… ▽ More In nonlinear dynamical systems, tipping refers to a critical transition from one steady state to another, typically catastrophic, steady state, often resulting from a saddle-node bifurcation. Recently, the machine-learning framework of parameter-adaptable reservoir computing has been applied to predict tipping in systems described by low-dimensional stochastic differential equations. However, anticipating tipping in complex spatiotemporal dynamical systems remains a significant open problem. The ability to forecast not only the occurrence but also the precise timing of such tipping events is crucial for providing the actionable lead time necessary for timely mitigation. By utilizing the mathematical approach of non-negative matrix factorization to generate dimensionally reduced spatiotemporal data as input, we exploit parameter-adaptable reservoir computing to accurately anticipate tipping. We demonstrate that the tipping time can be identified within a narrow prediction window across a variety of spatiotemporal dynamical systems, as well as in CMIP5 (Coupled Model Intercomparison Project 5) climate projections. Furthermore, we show that this reservoir-computing framework, utilizing reduced input data, is robust against common forecasting challenges and significantly alleviates the computational overhead associated with processing full spatiotemporal data. △ Less

Submitted 7 April, 2026; originally announced April 2026.

Comments: 26 pages, 25 figures

Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)

Authors: Julia Gonski, Jenni Ott, Shiva Abbaszadeh, Sagar Addepalli, Matteo Cremonesi, Jennet Dickinson, Giuseppe Di Guglielmo, Erdem Yigit Ertorer, Lindsey Gray, Ryan Herbst, Christian Herwig, Tae Min Hong, Benedikt Maier, Maryam Bayat Makou, David Miller, Mark S. Neubauer, Cristián Peña, Dylan Rankin, Seon-Hee, Seo, Giordon Stark, Alexander Tapper, Audrey Corbeil Therrien, Ioannis Xiotidis, Keisuke Yoshihara , et al. (99 additional authors not shown)

Abstract: The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilitie… ▽ More The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning (AI/ML), silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, novel codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing towards a successful transition to the new data frontier of fundamental science. △ Less

Submitted 24 July, 2026; v1 submitted 24 February, 2026; originally announced February 2026.

Comments: 123 pages, 53 figures

Systematic Design and Demonstration of Multipole, Coupled-Cavity Integrated Photonic Bandpass Filters with High FSR, High-Q Single-Mode Microresonators in Low-Loss Silicon Nitride Platform

Authors: Amir H Hosseinnia, Abdel Karim El Amili, Yu-hung Lai, Danny Eliyahu, Mohammad Enjavi, Lute Maleki, Ali Adibi

Abstract: Tunable, low-loss, narrowband, and frequency-stabilized filters play a critical role in the realization of transceivers for efficient signal processing in telecommunications and sensing applications in the presence of strong interference and noise. We systematically design, fabricate, and demonstrate multi-cavity, multipole integrated photonic bandpass filters with gigahertz to sub-gigahertz bandw… ▽ More Tunable, low-loss, narrowband, and frequency-stabilized filters play a critical role in the realization of transceivers for efficient signal processing in telecommunications and sensing applications in the presence of strong interference and noise. We systematically design, fabricate, and demonstrate multi-cavity, multipole integrated photonic bandpass filters with gigahertz to sub-gigahertz bandwidths. The filters feature ultra-wideband tunable center frequencies exceeding 400 GHz, ultra-low insertion loss, steep roll-off, and compact footprints, making them suitable for radio-frequency, microwave, and millimeter-wave front-end applications. Using a set of design principles for multi-cavity high-order filters together with supporting nanofabrication techniques in a silicon nitride platform, the demonstrated filters achieve record-high figures of merit and advance the state of the art in integrated photonic filtering for radio-frequency, microwave, and millimeter-wave systems. The resonator structures, which are the key building blocks enabling the reported performance, combine a large free spectral range of approximately 70 GHz with a high quality factor of 2.1 times ten to the power of seven. This performance is enabled by combining wide multimode waveguide segments with narrow single-mode regions connected through adiabatic tapers. To the best of our knowledge, the experimentally demonstrated bandwidth of 520 MHz, tuning range of one free spectral range, filter insertion loss of 2 dB, and out-of-band rejection ratios of up to 55 dB represent record performance metrics for integrated photonic filtering of radio-frequency, millimeter-wave, and terahertz signals. △ Less

Submitted 16 February, 2026; originally announced February 2026.

Real-time graph neural networks on FPGAs for the Belle II electromagnetic calorimeter

Authors: I. Haide, M. Neu, Y. Unno, T. Justinger, V. Dajaku, F. Baptist, T. Lobmaier, J. Becker, T. Ferber, H. Bae, A. Beaubien, J. Eppelt, R. Giordano, G. Heine, T. Koga, Y. -T. Lai, K. Miyabayashi, H. Nakazawa, M. Remnev, L. Reuter, K. Unger, R. van Tonder

Abstract: We present the development and evaluation of a real-time Graph Neural Network-based trigger module for the electromagnetic calorimeter of the Belle~II experiment at the SuperKEKB collider. The algorithm processes calorimeter trigger cells as graph nodes to perform clustering, feature extraction, and per-cluster signal classification with deterministic latency. The model predicts cluster positions… ▽ More We present the development and evaluation of a real-time Graph Neural Network-based trigger module for the electromagnetic calorimeter of the Belle~II experiment at the SuperKEKB collider. The algorithm processes calorimeter trigger cells as graph nodes to perform clustering, feature extraction, and per-cluster signal classification with deterministic latency. The model predicts cluster positions and energies and provides a signal classification score, enabling a more flexible clustering strategy than the baseline trigger algorithm. Implemented on an FPGA and integrated into the Belle~II trigger readout infrastructure for synchronous operation, the system sustains the MHz trigger throughput with an end-to-end latency of $3.168\,μ$s. The performance is evaluated on simulated events and collision data. The energy resolution is comparable to the baseline trigger, while the position resolution for high-energy clusters improves by up to 18% in the central detector region. Cluster purity increases by up to 20% at low energies for isolated clusters, and cluster efficiency improves by up to 20% for overlapping clusters. The signal classifier enables additional background suppression at fixed signal retention. These results demonstrate a first step towards GNN-based real-time reconstruction on FPGAs in a collider trigger. While the end-to-end latency exceeds the trigger decision budget, the system already sustains full operational conditions with 100% uptime. △ Less

Submitted 22 July, 2026; v1 submitted 16 February, 2026; originally announced February 2026.

Journal ref: JINST 21 (2026) 06, P06039

Physics-Informed Deep Operator Learning for Computational Hydraulics Modeling

Authors: Xiaofeng Liu, Yong G. Lai

Abstract: Traditional 2D hydraulic models face significant computational challenges that limit their applications that are time-sensitive or require many model evaluations. This study presents a physics-informed Deep Operator Network (DeepONet) framework for computational hydraulics modeling that learns the solution operator of the 2D shallow water equations (SWEs) to create fast surrogate models. The frame… ▽ More Traditional 2D hydraulic models face significant computational challenges that limit their applications that are time-sensitive or require many model evaluations. This study presents a physics-informed Deep Operator Network (DeepONet) framework for computational hydraulics modeling that learns the solution operator of the 2D shallow water equations (SWEs) to create fast surrogate models. The framework can operate in two modes: a purely data-driven SWE-DeepONet that learns from numerical solver such as SRH-2D, and a physics-informed PI-SWE-DeepONet that additionally incorporates the continuous SWEs as constraints during training. Based on a real-world case, steady flows in a reach of the Sacramento River in California, it is demonstrated that PI-SWE-DeepONet possesses much enhanced prediction capability than SWE-DeepONet when applied to out-of-distribution scenarios. The physics-informed model is shown to exhibit slower error growth and larger breakdown distances in comparison with SWE-DeepONet. The gain of the physics-informed training, however, comes with costs, chief among which are the simulated results have slightly higher errors for in-distribution cases. It reflects the existence of a tension between the two competing training objectives: fitting the results from the traditional hydraulic model and satisfying the continuous governing equations. In this study, guidelines are developed for selecting the appropriate approach based on a real-world case: PI-SWE-DeepONet is preferred for out-of-distribution predictions, uncertain training data, or when physical consistency is a priority, while SWE-DeepONet is recommended if the modeling objective is to replicate faithfully the traditional hydraulic model results within the training distribution. Other challenges are also discussed, such as the loss weighting approach. △ Less

Submitted 12 January, 2026; originally announced January 2026.

Non-Abelian gauge field optics in the time domain

Authors: Yucheng Lai, Yongliang Zhang, Kai Chang

Abstract: Artificial gauge fields open up burgeoning opportunities for wave engineering in different disciplines. So far,previous works have mostly focused on synthesizing spatial gauge fields, where the pseudo-magnetic fields lie at the heart of these phenomena. In this Letter, we generalize the paradigm of gauge field optics to the time domain by using time-varying media with rotating anisotropy. Dual to… ▽ More Artificial gauge fields open up burgeoning opportunities for wave engineering in different disciplines. So far,previous works have mostly focused on synthesizing spatial gauge fields, where the pseudo-magnetic fields lie at the heart of these phenomena. In this Letter, we generalize the paradigm of gauge field optics to the time domain by using time-varying media with rotating anisotropy. Dual to its spatial counterpart, the temporal gauge field induces a pseudo-electric field for optical pulses, leading to the spin-dependent longitudinal shift and Zitterbewegung for both trajectory and frequency. In addition, we analyze the temporal non-Abelian interference effect induced by temporally bounded non-Abelian gauge field media, which results in the temporal spin-precession and the temporal analogy of the non-Abelian Aharonov-Bohm effect. Our work not only fills the gap between synthetic gauge fields and time-varying physical systems, but also provides a fundamentally new approach for manipulating light with time-varying media. △ Less

Submitted 24 December, 2025; originally announced December 2025.

Aerogel RICH Counter at the Belle II Detector

Authors: I. Adachi, N. Akopov, D. Augueste, J. Bonis, L. Burmistrov, S. Dey, R. Dolenec, G. Ghevondyan, R. Giordano, A. Hvala, T. Iijima, S. Iwata, H. Kakuno, G. Karyan, H. Kawai, T. Kohriki, T. Konno, S. Korpar, P. Krizan, S. Kurokawa, Y. Lai, A. Lozar, M. Mrvar, G. Nazaryan, S. Nishida , et al. (14 additional authors not shown)

Abstract: We report on the design, operation, and performance of a novel proximity-focusing Ring Imaging Cherenkov (RICH) detector equipped with a multilayer focusing aerogel radiator, developed for the forward region of the Belle II spectrometer at the SuperKEKB $e^+e^-$ collider. The system achieves effective separation of charged pions, kaons, and protons across the full kinematic range of the experiment… ▽ More We report on the design, operation, and performance of a novel proximity-focusing Ring Imaging Cherenkov (RICH) detector equipped with a multilayer focusing aerogel radiator, developed for the forward region of the Belle II spectrometer at the SuperKEKB $e^+e^-$ collider. The system achieves effective separation of charged pions, kaons, and protons across the full kinematic range of the experiment, from 0.5 GeV/c to 4 GeV/c. To date, the detector has successfully operated in data-taking, contributing to the collection and analysis of nearly 600/fb of Belle II $e^+e^-$ collision data. △ Less

Submitted 23 December, 2025; v1 submitted 22 December, 2025; originally announced December 2025.

Comments: 25 pages, 30 figures. Corresponding authors: S. Korpar, S. Nishida

Optimal sparse networks for synchronization of semiconductor lasers

Authors: Li-Li Ye, Nathan Vigne, Fan-Yi Lin, Hui Cao, Ying-Cheng Lai

Abstract: The inevitable random frequency differences among semiconductor lasers present an obstacle to achieving their collective coherence, but previous worked showed that fully (all-to-all) coupled networks can still be synchronized even in the weakly coupling regime. An outstanding question is whether sparsely coupled network structures exist that lead to strong synchronization. This paper gives an affi… ▽ More The inevitable random frequency differences among semiconductor lasers present an obstacle to achieving their collective coherence, but previous worked showed that fully (all-to-all) coupled networks can still be synchronized even in the weakly coupling regime. An outstanding question is whether sparsely coupled network structures exist that lead to strong synchronization. This paper gives an affirmative answer: optimal sparse coupling configurations can be found which enables near-complete synchronization. Quite surprisingly, with respect to synchronization, certain sparse networks can outperform fully coupled networks, when the weights of coupling are placed dominantly on the laser pairs with large frequency differences. The counterintuitive phenomenon can be explained by a thermodynamic potential theory that maps the time-delay-induced phase dynamics to an energy landscape. These findings suggest a scalable and cost-effective approach to achieving robust, steady-state synchronization of semiconductor lasers in the presence of disorder and noise. △ Less

Submitted 5 November, 2025; originally announced November 2025.

Comments: 34 pages, 16 figures

Large-scale automatic carbon ion treatment planning for head and neck cancers via parallel multi-agent reinforcement learning

Authors: Jueye Zhang, Chao Yang, Youfang Lai, Kai-Wen Li, Wenting Yan, Yunzhou Xia, Haimei Zhang, Jingjing Zhou, Gen Yang, Chen Lin, Tian Li, Yibao Zhang

Abstract: Head-and-neck cancer (HNC) planning is difficult because multiple critical organs-at-risk (OARs) are close to complex targets. Intensity-modulated carbon-ion therapy (IMCT) offers superior dose conformity and OAR sparing but remains slow due to relative biological effectiveness (RBE) modeling, leading to laborious, experience-based, and often suboptimal tuning of many treatment-planning parameters… ▽ More Head-and-neck cancer (HNC) planning is difficult because multiple critical organs-at-risk (OARs) are close to complex targets. Intensity-modulated carbon-ion therapy (IMCT) offers superior dose conformity and OAR sparing but remains slow due to relative biological effectiveness (RBE) modeling, leading to laborious, experience-based, and often suboptimal tuning of many treatment-planning parameters (TPPs). Recent deep learning (DL) methods are limited by data bias and plan feasibility, while reinforcement learning (RL) struggles to efficiently explore the exponentially large TPP search space. We propose a scalable multi-agent RL (MARL) framework for parallel tuning of 45 TPPs in IMCT. It uses a centralized-training decentralized-execution (CTDE) QMIX backbone with Double DQN, Dueling DQN, and recurrent encoding (DRQN) for stable learning in a high-dimensional, non-stationary environment. To enhance efficiency, we (1) use compact historical DVH vectors as state inputs, (2) apply a linear action-to-value transform mapping small discrete actions to uniform parameter adjustments, and (3) design an absolute, clinically informed piecewise reward aligned with plan scores. A synchronous multi-process worker system interfaces with the PHOENIX TPS for parallel optimization and accelerated data collection. On a head-and-neck dataset (10 training, 10 testing), the method tuned 45 parameters simultaneously and produced plans comparable to or better than expert manual ones (relative plan score: RL $85.93\pm7.85%$ vs Manual $85.02\pm6.92%$), with significant (p-value $<$ 0.05) improvements for five OARs. The framework efficiently explores high-dimensional TPP spaces and generates clinically competitive IMCT plans through direct TPS interaction, notably improving OAR sparing. △ Less

Submitted 4 November, 2025; originally announced November 2025.

Deep-Learning-Empowered Programmable Topolectrical Circuits

Authors: Hao Jia, Shanglin Yang, Jiajun He, Shuo Liu, Haoxiang Chen, Ce Shang, Shaojie Ma, Peng Han, Ching Hua Lee, Zhen Gao, Yun Lai, Tie Jun Cui

Abstract: Topolectrical circuits provide a versatile platform for exploring and simulating modern physical models. However, existing approaches suffer from incomplete programmability and ineffective feature prediction and control mechanisms, hindering the investigation of physical phenomena on an integrated platform and limiting their translation into practical applications. Here, we present a deep learning… ▽ More Topolectrical circuits provide a versatile platform for exploring and simulating modern physical models. However, existing approaches suffer from incomplete programmability and ineffective feature prediction and control mechanisms, hindering the investigation of physical phenomena on an integrated platform and limiting their translation into practical applications. Here, we present a deep learning empowered programmable topolectrical circuits (DLPTCs) platform for physical modeling and analysis. By integrating fully independent, continuous tuning of both on site and off site terms of the lattice Hamiltonian, physics graph informed inverse state design, and immediate hardware verification, our system bridges the gap between theoretical modeling and practical realization. Through flexible control and adiabatic path engineering, we experimentally observe the boundary states without global symmetry in higher order topological systems, their adiabatic phase transitions, and the flat band like characteristic corresponding to Landau levels in the circuit. Incorporating a physics graph informed mechanism with a generative AI model for physics exploration, we realize arbitrary, position controllable on board Anderson localization, surpassing conventional random localization. Utilizing this unique capability with high fidelity hardware implementation, we further demonstrate a compelling cryptographic application: hash based probabilistic information encryption by leveraging Anderson localization with extensive disorder configurations, enabling secure delivery of full ASCII messages. △ Less

Submitted 28 October, 2025; originally announced October 2025.

Disorder-mediated synchronization resonance in coupled semiconductor lasers

Authors: Li-Li Ye, Nathan Vigne, Fan-Yi Lin, Hui Cao, Ying-Cheng Lai

Abstract: Disorder can profoundly influence synchronization in networks of nonlinear oscillators, sometimes enhancing coherence through external tuning. In semiconductor lasers, however, achieving high-quality steady-state synchronization is desired, while intrinsic and typically uncontrollable disorder poses a major challenge. Under fixed frequency disorder, we investigate homogeneous fully coupled externa… ▽ More Disorder can profoundly influence synchronization in networks of nonlinear oscillators, sometimes enhancing coherence through external tuning. In semiconductor lasers, however, achieving high-quality steady-state synchronization is desired, while intrinsic and typically uncontrollable disorder poses a major challenge. Under fixed frequency disorder, we investigate homogeneous fully coupled external-cavity semiconductor lasers governed by the complex, time-delayed Lang-Kobayashi equations with experimentally relevant parameters and identify an optimal coupling strength that maximizes steady-state synchronization in the weak-coupling regime, which we term disorder-mediated synchronization resonance. This optimum appears for any fixed configuration of intrinsic frequency detuning and scales inversely with the number of lasers, leading to a linear scaling of the total coupling cost with the number of lasers. A theory based on an effective thermodynamic potential explains this disorder-mediated optimization, revealing a general mechanism by which moderate coupling can overcome static heterogeneity in nonlinear physical systems. △ Less

Submitted 31 January, 2026; v1 submitted 8 September, 2025; originally announced September 2025.

Comments: 13 pages, 6 figures

Journal ref: Phys. Rev. Research 8, 013104 (2026)

Thickness-Induced Topological Phase Transition Investigated by Helicity Dependent Photocurrent in $α$-Sn/CdTe(110)

Authors: Tengfei Liu, Xiyu Hong, Zhe Li, Shenzhong Chen, Leyi Li, Xin-Yi Tang, Shuying Cheng, Yunfeng Lai, Yonghai Chen, Zhu Diao, Ke He, Qi-kun Xue, Jinling Yu

Abstract: $α$-Sn exhibits a rich topological phase diagram, yet experimental methods to tune and distinguish these phases remain limited. Here, we investigated the helicity-dependent photocurrent (HDPC) in $α$-Sn films of varying thickness grown on CdTe(110) by molecular beam epitaxy. The HDPC of the 5 nm $α… ▽ More $α$-Sn exhibits a rich topological phase diagram, yet experimental methods to tune and distinguish these phases remain limited. Here, we investigated the helicity-dependent photocurrent (HDPC) in $α$-Sn films of varying thickness grown on CdTe(110) by molecular beam epitaxy. The HDPC of the 5 nm $α$-Sn film shows an odd-function dependence on incident angle, whereas that of the 10 and 30 nm films exhibit an even-function dependence. Combined with high-resolution transmission electron microscopy (HR-TEM), point-group symmetry analysis, and first-principles calculations, it is revealed that a thickness-driven topological phase transition from a two dimensional (2D) to a three dimensional (3D) topological insulator occurs between 5 and 10 nm. These results demonstrate that HDPC serves as a sensitive diagnostic tool for topological phase transitions. The tunable electronic properties of $α$-Sn(110) films enable thickness- and strain-mediated control of topological states, establishing a versatile platform for exploring emerging topological phenomena and developing spin-based devices. △ Less

Submitted 4 September, 2025; originally announced September 2025.

Free Extension of Topological States via Double-zero-index Media

Authors: Rui Dong, Changhui Shen, Changqing Xu, Yun Lai, Ce Shang

Abstract: Topological states, known for their robustness against disorder, offer promising avenues for disorder-resistant devices. However, their intrinsic spatial confinement at interfaces imposes geometric constraints that limit the scalability of topological functionalities. Here, we propose a strategy to overcome this limitation by using double-zero-index media to expand topological interfaces. Although… ▽ More Topological states, known for their robustness against disorder, offer promising avenues for disorder-resistant devices. However, their intrinsic spatial confinement at interfaces imposes geometric constraints that limit the scalability of topological functionalities. Here, we propose a strategy to overcome this limitation by using double-zero-index media to expand topological interfaces. Although occupying finite space, these media are optically equivalent to infinitesimal points, effectively altering the geometry of topological interfaces and breaking conventional bulk-edge correspondence. This strategy enables the spatial expansion of uniform topological states beyond their native interface, offering new possibilities for topological photonic devices. We have verified this behavior through numerical simulations and microwave experiments in a two-dimensional photonic Su-Schrieffer-Heeger lattice. Our findings offer a universal framework to overcome the inherent dimensional limitations of topological states, with implications extending to general wave systems such as acoustic metamaterials. △ Less

Submitted 4 August, 2025; originally announced August 2025.

Real-time, Adaptive Radiological Anomaly Detection and Isotope Identification Using Non-negative Matrix Factorization

Authors: Chandler Jones, Mark Bandstra, Stefan Faaland, Yue Shi Lai, Nico Abgrall, Scott Suchyta, Reynold Cooper

Abstract: Spectroscopic anomaly detection and isotope identification algorithms are integral components in nuclear nonproliferation applications such as search operations. The task is especially challenging in the case of mobile detector systems due to the fact that the observed gamma-ray background changes more than for a static detector system, and a pretrained background model can easily find itself out… ▽ More Spectroscopic anomaly detection and isotope identification algorithms are integral components in nuclear nonproliferation applications such as search operations. The task is especially challenging in the case of mobile detector systems due to the fact that the observed gamma-ray background changes more than for a static detector system, and a pretrained background model can easily find itself out of domain. The result is that algorithms may exceed their intended false alarm rate, or sacrifice detection sensitivity in order to maintain the desired false alarm rate. Non-negative matrix factorization (NMF) has been shown to be a powerful tool for spectral anomaly detection and identification, but, like many similar algorithms that rely on data-driven background models, in its conventional implementation it is unable to update in real time to account for environmental changes that affect the background spectroscopic signature. We have developed a novel NMF-based algorithm that periodically updates its background model to accommodate changing environmental conditions. The Adaptive NMF algorithm involves fewer assumptions about its environment, making it more generalizable than existing NMF-based methods while maintaining or exceeding detection performance on simulated and real-world datasets. △ Less

Submitted 16 September, 2025; v1 submitted 14 July, 2025; originally announced July 2025.

Comments: 12 pages, 9 figures

Navigation of a Three-Link Microswimmer via Deep Reinforcement Learning

Authors: Yuyang Lai, Sina Heydari, On Shun Pak, Yi Man

Abstract: Motile microorganisms develop effective swimming gaits to adapt to complex biological environments. Translating this adaptability to smart microrobots presents significant challenges in motion planning and stroke design. In this work, we explore the use of reinforcement learning (RL) to develop stroke patterns for targeted navigation in a three-link swimmer model at low Reynolds numbers. Specifica… ▽ More Motile microorganisms develop effective swimming gaits to adapt to complex biological environments. Translating this adaptability to smart microrobots presents significant challenges in motion planning and stroke design. In this work, we explore the use of reinforcement learning (RL) to develop stroke patterns for targeted navigation in a three-link swimmer model at low Reynolds numbers. Specifically, we design two RL-based strategies: one focusing on maximizing velocity (Velocity-Focused Strategy) and another balancing velocity with energy consumption (Energy-Aware Strategy). Our results demonstrate how the use of different reward functions influences the resulting stroke patterns developed via RL, which are compared with those obtained from traditional optimization methods. Furthermore, we showcase the capability of the RL-powered swimmer in adapting its stroke patterns in performing different navigation tasks, including tracing complex trajectories and pursuing moving targets. Taken together, this work highlights the potential of reinforcement learning as a versatile tool for designing efficient and adaptive microswimmers capable of sophisticated maneuvers in complex environments. △ Less

Submitted 29 May, 2025; originally announced June 2025.

Efficient training for large-scale optical neural network using an evolutionary strategy and attention pruning

Authors: Zhiwei Yang, Zeyang Fan, Yihang Lai, Qi Chen, Tian Zhang, Jian Dai, Kun Xu

Abstract: MZI-based block optical neural networks (BONNs), which can achieve large-scale network models, have increasingly drawn attentions. However, the robustness of the current training algorithm is not high enough. Moreover, large-scale BONNs usually contain numerous trainable parameters, resulting in expensive computation and power consumption. In this article, by pruning matrix blocks and directly opt… ▽ More MZI-based block optical neural networks (BONNs), which can achieve large-scale network models, have increasingly drawn attentions. However, the robustness of the current training algorithm is not high enough. Moreover, large-scale BONNs usually contain numerous trainable parameters, resulting in expensive computation and power consumption. In this article, by pruning matrix blocks and directly optimizing the individuals in population, we propose an on-chip covariance matrix adaptation evolution strategy and attention-based pruning (CAP) algorithm for large-scale BONNs. The calculated results demonstrate that the CAP algorithm can prune 60% and 80% of the parameters for MNIST and Fashion-MNIST datasets, respectively, while only degrades the performance by 3.289% and 4.693%. Considering the influence of dynamic noise in phase shifters, our proposed CAP algorithm (performance degradation of 22.327% for MNIST dataset and 24.019% for Fashion-MNIST dataset utilizing a poor fabricated chip and electrical control with a standard deviation of 0.5) exhibits strongest robustness compared with both our previously reported block adjoint training algorithm (43.963% and 41.074%) and the covariance matrix adaptation evolution strategy (25.757% and 32.871%), respectively. Moreover, when 60% of the parameters are pruned, the CAP algorithm realizes 88.5% accuracy in experiment for the simplified MNIST dataset, which is similar to the simulation result without noise (92.1%). Additionally, we simulationally and experimentally demonstrate that using MZIs with only internal phase shifters to construct BONNs is an efficient way to reduce both the system area and the required trainable parameters. Notably, our proposed CAP algorithm show excellent potential for larger-scale network models and more complex tasks. △ Less

Submitted 19 May, 2025; originally announced May 2025.

Tunable hinge skin states in a hybrid skin-topological sonic crystal

Authors: Y. X. Fang, W. H. Zhu, Y. P. Lai, Y. Li, S. Q. Wu

Abstract: Higher-order topological states in sound have played a pivotal role in understanding the intricate physics underlying sound transport, giving rise to new strategy of manipulating sound. Here we report tunable structure for hinge skin states in a non-Hermitian acoustic metamaterial with hybrid skin-topological effect. Our finding shows that when on-site gain and loss are exquisitely introduced into… ▽ More Higher-order topological states in sound have played a pivotal role in understanding the intricate physics underlying sound transport, giving rise to new strategy of manipulating sound. Here we report tunable structure for hinge skin states in a non-Hermitian acoustic metamaterial with hybrid skin-topological effect. Our finding shows that when on-site gain and loss are exquisitely introduced into acoustic topological insulators, chiral edge modes in Hermitian counterpart would respectively become amplified or attenuated at zigzag boundaries. If adjacent gain and loss boundaries are intentionally constructed, hinge skin states would take place at their intersections. By strategically combining non-Hermitian and topological physics, we successfully reveal how higher-order hinge modes originate from lower-order surface states and demonstrate flexible acoustic steering in tunable non-Hermitian blocks. Our findings unveil that skin-topological effect may hold significant applications in designing interesting acoustic devices with unconventional functions such as multidimensional acoustic control and accurate energy harvesting. △ Less

Submitted 9 May, 2025; originally announced May 2025.

Comments: 25 pages, 8 figures

Electromagnetic Duality Symmetry-Protected Dirac-Like Cones

Authors: Muxuan Yang, Dongyang Yan, Lei Gao, Wei Liu, Yun Lai, Yadong Xu, Zhi Hong Hang, Jie Luo

Abstract: Dirac-like cones, featuring conical linear dispersions intersecting with flat bands, typically arise from accidental degeneracy of multiple modes that requires precise tuning of material and structural parameters, inherently limiting their robustness and applications. In this work, by introducing electromagnetic duality symmetry into photonic crystals, we demonstrate the emergence of intrinsically… ▽ More Dirac-like cones, featuring conical linear dispersions intersecting with flat bands, typically arise from accidental degeneracy of multiple modes that requires precise tuning of material and structural parameters, inherently limiting their robustness and applications. In this work, by introducing electromagnetic duality symmetry into photonic crystals, we demonstrate the emergence of intrinsically robust deterministic Dirac-like cones. We show that such symmetry (achieved through either self-dual particles or non-self-dual particle clusters with duality-glide symmetry) enforces double degeneracies for band structures of photonic crystals. Furthermore, by harnessing the joint duality-structural symmetry, multiple deterministic Dirac-like cones exhibiting exceptional resilience to lattice size variations can be obtained. Our introduction of an extra symmetry into photonic crystals establishes a profound connection between duality symmetry and Dirac physics, providing a robust platform for advanced photonic band engineering. △ Less

Submitted 18 March, 2025; originally announced March 2025.

Journal ref: ACS Photonics 2025

Design of the Global Reconstruction Logic in the Belle II Level-1 Trigger system

Authors: Y. -T. Lai, T. Koga, Y. Iwasaki, Y. Ahn, H. Bae, M. Campajola, B. G. Cheon, H. -E. Cho, T. Ferber, I. Haide, G. Heine, C. -L. Hsu, C. Kiesling, C. -H. Kim, J. B. Kim, K. Kim, S. H. Kim, I. S. Lee, M. J. Lee, Y. P. Liao, J. Lin, A. Little, H. K. Moon, H. Nakazawa, M. Neu , et al. (10 additional authors not shown)

Abstract: The Belle~II experiment is designed to search for physics beyond the Standard Model by investigating rare decays at the SuperKEKB \(e^{+}e^{-}\) collider. Owing to the significant beam background at high luminosity, the data acquisition system employs a hardware-based Level-1~Trigger to reduce the readout data throughput by selecting collision events of interest in real time. The Belle~II Level-1~… ▽ More The Belle~II experiment is designed to search for physics beyond the Standard Model by investigating rare decays at the SuperKEKB \(e^{+}e^{-}\) collider. Owing to the significant beam background at high luminosity, the data acquisition system employs a hardware-based Level-1~Trigger to reduce the readout data throughput by selecting collision events of interest in real time. The Belle~II Level-1~Trigger system utilizes FPGAs to reconstruct various detector observables from the raw data for trigger decision-making. The Global Reconstruction Logic receives these processed observables from four sub-trigger systems and provides a global summary for the final trigger decision. Its logic encompasses charged particle tracking, matching between sub-triggers, and the identification of special event topologies associated with low-multiplicity decays. This article discusses the hardware devices, FPGA firmware, integration with peripheral systems, and the design and performance of the trigger algorithms implemented within the Global Reconstruction Logic. △ Less

Submitted 3 March, 2025; originally announced March 2025.

Comments: 10 pages, 12 figures

Journal ref: Nuclear Instruments and Methods in Physics Research Section A, Volume 1078, 2025, 170577

Wave-propagation Based Analysis of the Magnetostatic Waves in Ferrite Films Excited by Metallic Transducers

Authors: Zhizhi Zhang, Yuanming Lai, Qian Liu, Xiongzhang Liu, Chongsheng Wu, Peng Yan

Abstract: It is conventional wisdom that the spectra of the impedances of magnetostatic waves (MSWs) determine the transmissions of MSW devices. In this work, we show that the characteristics of propagating MSWs have critical impacts on the characteristics of transmissions. A wave-propagation based analysis considering the inhomogeneous distributions of magnetic fields is presented for investigating the pro… ▽ More It is conventional wisdom that the spectra of the impedances of magnetostatic waves (MSWs) determine the transmissions of MSW devices. In this work, we show that the characteristics of propagating MSWs have critical impacts on the characteristics of transmissions. A wave-propagation based analysis considering the inhomogeneous distributions of magnetic fields is presented for investigating the propagations of MSWs. Based on the analysis, it is demonstrated that the metallic nature of transducers causes the high insertion losses in high-frequency bands, while the dips and severe in-band ripples in low-frequency bands are resulted from the complicated interference between the multiple width modes. Simulations in HFSS verify the analysis with good agreements. Our work advances the understanding of MSWs propagating in ferrite films with metallic structures and paves the way to designing MSW devices aimed at implantation in microwave systems. △ Less

Submitted 19 June, 2025; v1 submitted 20 February, 2025; originally announced February 2025.

The Effect of Water Contamination on the Aging of a Dual-Carbon Lithium-Ion Capacitor Employing LiFSI-Based Electrolyte

Authors: Philipp Schweigart, Johan Hamonnet, Obinna Egwu Eleri, Laura King, Samson Yuxiu Lai, Ann Mari Svensson

Abstract: Fabricating electrochemical energy storage devices demands significant energy for drying cell components to ensure optimal performance. The development of new, water-tolerant materials would represent a tremendous advance in cost savings and sustainability. Although it is generally established that water deteriorates cell performance, there are few systematic studies on the maximum amount of toler… ▽ More Fabricating electrochemical energy storage devices demands significant energy for drying cell components to ensure optimal performance. The development of new, water-tolerant materials would represent a tremendous advance in cost savings and sustainability. Although it is generally established that water deteriorates cell performance, there are few systematic studies on the maximum amount of tolerable water contamination, and most of the studies employed the electrolyte salt $\mathrm{LiPF_6}$, which inevitably decomposes upon exposure to humidity. In this work, the potential of using the non-hydrolyzing salt LiFSI is explored with respect to its performance in Li-ion capacitor cells based on activated carbon (AC) and pre-lithiated graphite (Gr). Water is deliberately added in various amounts (950, 2300, and 6000 ppm), and its effect on the electrochemical performance and aging of AC and Gr electrodes is systematically studied. While the addition of 950 ppm water has no evident impact on capacity retention (96% after 2000 cycles), the addition of 2300 ppm water or 6000 ppm water shows a distinct capacity fade, attributed to a significant loss of lithium inventory and increased resistivity due to irreversible reactions of the water with lithiated Gr. Post-mortem analysis reveals that water promotes the oxidative and reductive decomposition of LiFSI on AC and lithiated Gr, respectively. A significant thickening of the SEI on Gr is observed as the water concentration is increased. △ Less

Submitted 18 February, 2025; originally announced February 2025.

Ultrasensitive Higher-Order Exceptional Points via Non-Hermitian Zero-Index Materials

Authors: Dongyang Yan, Alexander S. Shalin, Yongxing Wang, Yun Lai, Yadong Xu, Zhi Hong Hang, Fang Cao, Lei Gao, Jie Luo

Abstract: Higher-order exceptional points (EPs) in optical structures enable ultra-sensitive responses to perturbations. However, previous investigations on higher-order EPs have predominantly focused on coupled systems, leaving their fundamental physics in open scattering systems largely unexplored. Here, we harness wave interference to realize higher-order EPs in non-Hermitian zero-index materials connect… ▽ More Higher-order exceptional points (EPs) in optical structures enable ultra-sensitive responses to perturbations. However, previous investigations on higher-order EPs have predominantly focused on coupled systems, leaving their fundamental physics in open scattering systems largely unexplored. Here, we harness wave interference to realize higher-order EPs in non-Hermitian zero-index materials connected to multiple open channels. Specifically, we show that a three-channel model can give rise to three interesting types of third-order EPs: lasing EP, reflecting EP, and absorbing EP. Notably, near the third-order absorbing EP, we show ultrasensitivity -- a drastic change in output power in response to perturbations at the operating frequency -- in a purely lossy system. These findings pave the way for achieving higher-order and even arbitrary-order EPs in open scattering systems, offering significant potential for advanced sensing applications. △ Less

Submitted 17 April, 2025; v1 submitted 14 January, 2025; originally announced January 2025.

Report number: Physical Review Letters 134, 243802 (2025)

Journal ref: Physical Review Letters 134, 243802 (2025)

Metamaterial sound absorbers based on microperforated panels: an approach toward enhanced flexibility and near-limit broadband performance

Authors: Jinjie Shi, Jie Luo, Chenkai Liu, Hongchen Chu, Yongxin Jing, Changqing Xu, Xiaozhou Liu, Jensen Li, Yun Lai

Abstract: Traditional microperforated panels (MPPs) and metamaterial-based sound absorbers rely on local resonances or multi-resonator designs, which limit their bandwidth, angular applicability, and ease of fabrication. Leveraging the reciprocity theorem and cavity resonances, we introduce a new class of robust MPP absorbers, termed meta-MPPs, capable of achieving ultrabroadband near-total sound absorption… ▽ More Traditional microperforated panels (MPPs) and metamaterial-based sound absorbers rely on local resonances or multi-resonator designs, which limit their bandwidth, angular applicability, and ease of fabrication. Leveraging the reciprocity theorem and cavity resonances, we introduce a new class of robust MPP absorbers, termed meta-MPPs, capable of achieving ultrabroadband near-total sound absorption across a range of 0.37 to 10 kHz. These absorbers demonstrate average performance exceeding that of traditional MPPs by over 100%, approaching the theoretical causality limit. Notably, their absorption performance can be tuned between angularly asymmetric and omnidirectional modes and remains highly robust to variations in MPP parameters and geometrical configurations. Validated through simulations and experiments, our findings present a simpler, more robust, and highly adaptable solution for noise control. △ Less

Submitted 21 January, 2025; v1 submitted 12 January, 2025; originally announced January 2025.

Overview of the Head and Neck Tumor Segmentation for Magnetic Resonance Guided Applications (HNTS-MRG) 2024 Challenge

Authors: Kareem A. Wahid, Cem Dede, Dina M. El-Habashy, Serageldin Kamel, Michael K. Rooney, Yomna Khamis, Moamen R. A. Abdelaal, Sara Ahmed, Kelsey L. Corrigan, Enoch Chang, Stephanie O. Dudzinski, Travis C. Salzillo, Brigid A. McDonald, Samuel L. Mulder, Lucas McCullum, Qusai Alakayleh, Carlos Sjogreen, Renjie He, Abdallah S. R. Mohamed, Stephen Y. Lai, John P. Christodouleas, Andrew J. Schaefer, Mohamed A. Naser, Clifton D. Fuller

Abstract: Magnetic resonance (MR)-guided radiation therapy (RT) is enhancing head and neck cancer (HNC) treatment through superior soft tissue contrast and longitudinal imaging capabilities. However, manual tumor segmentation remains a significant challenge, spurring interest in artificial intelligence (AI)-driven automation. To accelerate innovation in this field, we present the Head and Neck Tumor Segment… ▽ More Magnetic resonance (MR)-guided radiation therapy (RT) is enhancing head and neck cancer (HNC) treatment through superior soft tissue contrast and longitudinal imaging capabilities. However, manual tumor segmentation remains a significant challenge, spurring interest in artificial intelligence (AI)-driven automation. To accelerate innovation in this field, we present the Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Challenge, a satellite event of the 27th International Conference on Medical Image Computing and Computer Assisted Intervention. This challenge addresses the scarcity of large, publicly available AI-ready adaptive RT datasets in HNC and explores the potential of incorporating multi-timepoint data to enhance RT auto-segmentation performance. Participants tackled two HNC segmentation tasks: automatic delineation of primary gross tumor volume (GTVp) and gross metastatic regional lymph nodes (GTVn) on pre-RT (Task 1) and mid-RT (Task 2) T2-weighted scans. The challenge provided 150 HNC cases for training and 50 for testing, hosted on Grand Challenge using a Docker submission framework. In total, 19 independent teams from across the world qualified by submitting both their algorithms and corresponding papers, resulting in 18 submissions for Task 1 and 15 submissions for Task 2. Evaluation using the mean aggregated Dice Similarity Coefficient showed top-performing AI methods achieved scores of 0.825 in Task 1 and 0.733 in Task 2. These results surpassed clinician interobserver variability benchmarks, marking significant strides in automated tumor segmentation for MR-guided RT applications in HNC. △ Less

Submitted 27 November, 2024; v1 submitted 27 November, 2024; originally announced November 2024.

Comments: For HNTS-MRG 2024 volume of Lecture Notes in Computer Science

Reconstructing dynamics from sparse observations with no training on target system

Authors: Zheng-Meng Zhai, Jun-Yin Huang, Benjamin D. Stern, Ying-Cheng Lai

Abstract: In applications, an anticipated situation is where the system of interest has never been encountered before and sparse observations can be made only once. Can the dynamics be faithfully reconstructed from the limited observations without any training data? This problem defies any known traditional methods of nonlinear time-series analysis as well as existing machine-learning methods that typically… ▽ More In applications, an anticipated situation is where the system of interest has never been encountered before and sparse observations can be made only once. Can the dynamics be faithfully reconstructed from the limited observations without any training data? This problem defies any known traditional methods of nonlinear time-series analysis as well as existing machine-learning methods that typically require extensive data from the target system for training. We address this challenge by developing a hybrid transformer and reservoir-computing machine-learning scheme. The key idea is that, for a complex and nonlinear target system, the training of the transformer can be conducted not using any data from the target system, but with essentially unlimited synthetic data from known chaotic systems. The trained transformer is then tested with the sparse data from the target system. The output of the transformer is further fed into a reservoir computer for predicting the long-term dynamics or the attractor of the target system. The power of the proposed hybrid machine-learning framework is demonstrated using a large number of prototypical nonlinear dynamical systems, with high reconstruction accuracy even when the available data is only 20% of that required to faithfully represent the dynamical behavior of the underlying system. The framework provides a paradigm of reconstructing complex and nonlinear dynamics in the extreme situation where training data does not exist and the observations are random and sparse. △ Less

Submitted 28 October, 2024; originally announced October 2024.

Comments: 31 pages, 21 figures

High-order exceptional points and stochastic resonance in pseudo-Hermitian systems

Authors: Shirin Panahi, Li-Li Ye, Ying-Cheng Lai

Abstract: Exceptional points, a remarkable phenomenon in physical systems, have been exploited for sensing applications. It has been demonstrated recently that it can also utilize as sensory threshold in which the interplay between exceptional-point dynamics and noise can lead to enhanced performance. Most existing works focused on second-order exceptional points. We investigate the stochastic dynamics asso… ▽ More Exceptional points, a remarkable phenomenon in physical systems, have been exploited for sensing applications. It has been demonstrated recently that it can also utilize as sensory threshold in which the interplay between exceptional-point dynamics and noise can lead to enhanced performance. Most existing works focused on second-order exceptional points. We investigate the stochastic dynamics associated with high-order exceptional points with a particular eye towards optimizing sensing performance by developing a theoretical framework based on pseudo-Hermiticity. Our analysis reveals three distinct types of frequency responses to external perturbations. A broad type of stochastic resonance is uncovered where, as the noise amplitude increases, the signal-to-noise ratio reaches a global maximum rapidly but with a slow decaying process afterwards, indicating achievable high performance in a wide range of the noise level. These results suggest that stochastic high-order exceptional-point dynamics can be exploited for applications in signal processing and sensor technologies. △ Less

Submitted 17 October, 2024; originally announced October 2024.

Comments: 13 pages, 9 figures

Data-driven model discovery with Kolmogorov-Arnold networks

Authors: Mohammadamin Moradi, Shirin Panahi, Erik M. Bollt, Ying-Cheng Lai

Abstract: Data-driven model discovery of complex dynamical systems is typically done using sparse optimization, but it has a fundamental limitation: sparsity in that the underlying governing equations of the system contain only a small number of elementary mathematical terms. Examples where sparse optimization fails abound, such as the classic Ikeda or optical-cavity map in nonlinear dynamics and a large va… ▽ More Data-driven model discovery of complex dynamical systems is typically done using sparse optimization, but it has a fundamental limitation: sparsity in that the underlying governing equations of the system contain only a small number of elementary mathematical terms. Examples where sparse optimization fails abound, such as the classic Ikeda or optical-cavity map in nonlinear dynamics and a large variety of ecosystems. Exploiting the recently articulated Kolmogorov-Arnold networks, we develop a general model-discovery framework for any dynamical systems including those that do not satisfy the sparsity condition. In particular, we demonstrate non-uniqueness in that a large number of approximate models of the system can be found which generate the same invariant set with the correct statistics such as the Lyapunov exponents and Kullback-Leibler divergence. An analogy to shadowing of numerical trajectories in chaotic systems is pointed out. △ Less

Submitted 23 September, 2024; originally announced September 2024.

Comments: 6 pages, 4 figures

Initial Experience of Metabolic Imaging with Hyperpolarized [1-13C]pyruvate MRI in Kidney Transplant Patients

Authors: Xiaoxi Liu, Ying-Chieh Lai., Di Cui, Shiang-Cheng Kung, Meyeon Park, Laszik Zoltan, Peder E. Z. Larson, Zhen J. Wang

Abstract: BACKGROUND: Kidney transplant is the treatment of choice for patients with end-stage renal disease. Early detection of allograft injury is important to delay or prevent irreversible damage. PURPOSE: To investigate the feasibility of hyperpolarized (HP) [1-13C]pyruvate MRI for assessing kidney allograft metabolism. SUBJECTS: 6 participants (mean age, 45.2 +- 12.4 years, 2 females) scheduled for kid… ▽ More BACKGROUND: Kidney transplant is the treatment of choice for patients with end-stage renal disease. Early detection of allograft injury is important to delay or prevent irreversible damage. PURPOSE: To investigate the feasibility of hyperpolarized (HP) [1-13C]pyruvate MRI for assessing kidney allograft metabolism. SUBJECTS: 6 participants (mean age, 45.2 +- 12.4 years, 2 females) scheduled for kidney allograft biopsy and 5 patients (mean age, 59.6 +- 10.4 years, 2 females) with renal cell carcinoma (RCC). ASSESSMENT: Five of the six kidney allograft participants underwent biopsy after MRI. Estimated glomerular filtration rate (eGFR) and urine protein-to-creatine ratio (uPCR) were collected within 4 weeks of MRI. Kidney metabolism was quantified from HP [1-13C]pyruvate MRI using the lactate-to-pyruvate ratio in allograft kidneys and non-tumor bearing kidneys from RCC patients. RESULTS: Biopsy was performed a mean of 9 days (range 5-19 days) after HP [1-13C]pyruvate MRI. Three biopsies were normal, one showed low-grade fibrosis and one showed moderate microvascular inflammation. All had stable functioning allografts with eGFR > 60 mL/min/1.73 m2 and normal uPCR. One participant who did not undergo biopsy had reduced eGFR of 49 mL/min/1.73 m2 and elevated uPCR. The mean lactate-to-pyruvate ratio was 0.373 in participants with normal findings (n = 3) and 0.552 in participants with abnormal findings (n = 2). The lactate-to-pyruvate ratio was highest (0.847) in the participant with reduced eGFR and elevated uPRC. Native non-tumor bearing kidneys had a mean lactate-to-pyruvate ratio of 0.309. DATA CONCLUSION: Stable allografts with normal findings at biopsy showed lactate-to-pyruvate ratios similar to native non-tumor bearing kidneys, whereas allografts with abnormal findings showed higher lactate-to-pyruvate ratios. △ Less

Submitted 10 September, 2024; originally announced September 2024.

Comments: J Magn Reson Imaging (2024)

Controlling nonergodicity in quantum many-body systems by reinforcement learning

Authors: Li-Li Ye, Ying-Cheng Lai

Abstract: Finding optimal control strategies to suppress quantum thermalization for arbitrarily initial states, the so-called quantum nonergodicity control, is important for quantum information science and technologies. Previous control methods largely relied on theoretical model of the target quantum system, but invertible model approximations and inaccuracies can lead to control failures. We develop a mod… ▽ More Finding optimal control strategies to suppress quantum thermalization for arbitrarily initial states, the so-called quantum nonergodicity control, is important for quantum information science and technologies. Previous control methods largely relied on theoretical model of the target quantum system, but invertible model approximations and inaccuracies can lead to control failures. We develop a model-free and deep-reinforcement learning (DRL) framework for quantum nonergodicity control. It is a machine-learning method with the unique focus on balancing exploration and exploitation strategies to maximize the cumulative rewards so as to preserve the initial memory in the time-dependent nonergodic metrics over a long stretch of time. We use the paradigmatic one-dimensional tilted Fermi-Hubbard system to demonstrate that the DRL agent can efficiently learn the quantum many-body system solely through the interactions with the environment. The optimal policy obtained by the DRL provides broader control scenarios for managing nonergodicity in the phase diagram as compared to, e.g., the specific protocol for Wannier-Stark localization. The continuous control protocols and observations are experimentally feasible. The model-free nature of DRL and its versatile search space for control functions render promising nonergodicity control in more complex quantum many-body systems. △ Less

Submitted 4 March, 2025; v1 submitted 21 August, 2024; originally announced August 2024.

Comments: 20 pages, 9 figures

Journal ref: Phys. Rev. Research 7, 013256, 2025

Room temperature operation of germanium-silicon single-photon avalanche diode

Authors: Neil Na, Yen-Cheng Lu, Yu-Hsuan Liu, Po-Wei Chen, Ying-Chen Lai, You-Ru Lin, Chung-Chih Lin, Tim Shia, Chih-Hao Cheng, Shu-Lu Chen

Abstract: The ability to detect single photons has led to the advancement of numerous research fields. Although various types of single-photon detector have been developed, because of two main factors - that is, (1) the need for operating at cryogenic temperature and (2) the incompatibility with complementary metal-oxide-semiconductor (CMOS) fabrication processes - so far, to our knowledge, only Si-based si… ▽ More The ability to detect single photons has led to the advancement of numerous research fields. Although various types of single-photon detector have been developed, because of two main factors - that is, (1) the need for operating at cryogenic temperature and (2) the incompatibility with complementary metal-oxide-semiconductor (CMOS) fabrication processes - so far, to our knowledge, only Si-based single-photon avalanche diode (SPAD) has gained mainstream success and has been used in consumer electronics. With the growing demand to shift the operation wavelength from near-infrared to short-wavelength infrared (SWIR) for better safety and performance, an alternative solution is required because Si has negligible optical absorption for wavelengths beyond 1 μm. Here we report a CMOS-compatible, high-performing germanium-silicon SPAD operated at room temperature, featuring a noise-equivalent power improvement over the previous Ge-based SPADs by 2-3.5 orders of magnitude. Key parameters such as dark count rate, single-photon detection probability at 1,310 nm, timing jitter, after-pulsing characteristic time and after-pulsing probability are, respectively, measured as 19 kHz μm^2, 12%, 188 ps, ~90 ns and <1%, with a low breakdown voltage of 10.26 V and a small excess bias of 0.75 V. Three-dimensional point-cloud images are captured with direct time-of-flight technique as proof of concept. This work paves the way towards using single-photon-sensitive SWIR sensors, imagers and photonic integrated circuits in everyday life. △ Less

Submitted 17 September, 2024; v1 submitted 14 July, 2024; originally announced July 2024.

Comments: accepted manuscript

Journal ref: Nature 627, 295 (2024)

Studies of Cherenkov Photon Production in PbF$_2$ Crystals using Proton Beams at Fermilab

Authors: Thomas Anderson, Alberto Belloni, Grace Cummings, Sarah Eno, Nora Fischer, Liang Guan, Yuxiang Guo, Robert Hirosky, James Hirschauer, Yihui Lai, Daniel Levin, Hui-Chi Lin, Mekhala Paranjpe, Jianming Qian, Bing Zhou, Junjie Zhu, Ren-Yuan Zhu

Abstract: Future lepton colliders such as the FCC-ee, CEPC, ILC, or a muon collider will collect large data samples that allow precision physics studies with unprecedented accuracy, especially when the data is collected by innovative state-of-the-art detectors. An electromagnetic calorimeter based on scintillating crystals, designed to separately record Cherenkov and scintillation light, can achieve precisi… ▽ More Future lepton colliders such as the FCC-ee, CEPC, ILC, or a muon collider will collect large data samples that allow precision physics studies with unprecedented accuracy, especially when the data is collected by innovative state-of-the-art detectors. An electromagnetic calorimeter based on scintillating crystals, designed to separately record Cherenkov and scintillation light, can achieve precision measurements of electrons and photons without sacrificing jet energy resolution, given adequate light collection efficiency and separation. This paper presents initial measurements from a program aimed at developing such a calorimeter system for future colliders. We focus on using PbF2 crystals to enhance the understanding of Cherenkov light collection, marking the first step in this endeavor. △ Less

Submitted 5 December, 2024; v1 submitted 10 July, 2024; originally announced July 2024.

Comments: 12 pages, 14 figures

Journal ref: Nuclear Instruments and Methods A 1072 (2025) 170109

The Belle II Detector Upgrades Framework Conceptual Design Report

Authors: H. Aihara, A. Aloisio, D. P. Auguste, M. Aversano, M. Babeluk, S. Bahinipati, Sw. Banerjee, M. Barbero, J. Baudot, A. Beaubien, F. Becherer, T. Bergauer, F. U. Bernlochner., V. Bertacchi, G. Bertolone, C. Bespin, M. Bessner, S. Bettarini, A. J. Bevan, B. Bhuyan, M. Bona, J. F. Bonis, J. Borah, F. Bosi, R. Boudagga , et al. (186 additional authors not shown)

Abstract: We describe the planned near-term and potential longer-term upgrades of the Belle II detector at the SuperKEKB electron-positron collider operating at the KEK laboratory in Tsukuba, Japan. These upgrades will allow increasingly sensitive searches for possible new physics beyond the Standard Model in flavor, tau, electroweak and dark sector physics that are both complementary to and competitive wit… ▽ More We describe the planned near-term and potential longer-term upgrades of the Belle II detector at the SuperKEKB electron-positron collider operating at the KEK laboratory in Tsukuba, Japan. These upgrades will allow increasingly sensitive searches for possible new physics beyond the Standard Model in flavor, tau, electroweak and dark sector physics that are both complementary to and competitive with the LHC and other experiments. △ Less

Submitted 4 July, 2024; v1 submitted 26 June, 2024; originally announced June 2024.

Comments: Editor: F. Forti 170 pages

Report number: KEK-REPORT-2024-1, BELLE2-REPORT-2024-042

Entanglement engineering of optomechanical systems by reinforcement learning

Authors: Li-Li Ye, Christian Arenz, Joseph M. Lukens, Ying-Cheng Lai

Abstract: Entanglement is fundamental to quantum information science and technology, yet controlling and manipulating entanglement -- so-called entanglement engineering -- for arbitrary quantum systems remains a formidable challenge. There are two difficulties: the fragility of quantum entanglement and its experimental characterization. We develop a model-free deep reinforcement-learning (RL) approach to en… ▽ More Entanglement is fundamental to quantum information science and technology, yet controlling and manipulating entanglement -- so-called entanglement engineering -- for arbitrary quantum systems remains a formidable challenge. There are two difficulties: the fragility of quantum entanglement and its experimental characterization. We develop a model-free deep reinforcement-learning (RL) approach to entanglement engineering, in which feedback control together with weak continuous measurement and partial state observation is exploited to generate and maintain desired entanglement. We employ quantum optomechanical systems with linear or nonlinear photon-phonon interactions to demonstrate the workings of our machine-learning-based entanglement engineering protocol. In particular, the RL agent sequentially interacts with one or multiple parallel quantum optomechanical environments, collects trajectories, and updates the policy to maximize the accumulated reward to create and stabilize quantum entanglement over an arbitrary amount of time. The machine-learning-based model-free control principle is applicable to the entanglement engineering of experimental quantum systems in general. △ Less

Submitted 31 December, 2024; v1 submitted 6 June, 2024; originally announced June 2024.

Comments: 17 pages, 10 figures

Journal ref: APL Mach. Learn. 3, 016107 (2025)

Magnetic Relaxometry of Methemoglobin by Widefield Nitrogen-Vacancy Microscopy

Authors: Suvechhya Lamichhane, Evelyn Carreto Guevara, Ilja Fescenko, Sy-Hwang Liou, Rebecca Y. Lai, Abdelghani Laraoui

Abstract: Hemoglobin (Hb) is a multifaceted protein, classified as a metalloprotein, chromoprotein, and globulin. It incorporates iron, which plays a crucial role in transporting oxygen within red blood cells. Hb functions by carrying oxygen from the respiratory organs to diverse tissues in the body, where it releases oxygen to fuel aerobic respiration, thus supporting the organism's metabolic processes. Hb… ▽ More Hemoglobin (Hb) is a multifaceted protein, classified as a metalloprotein, chromoprotein, and globulin. It incorporates iron, which plays a crucial role in transporting oxygen within red blood cells. Hb functions by carrying oxygen from the respiratory organs to diverse tissues in the body, where it releases oxygen to fuel aerobic respiration, thus supporting the organism's metabolic processes. Hb can exist in several forms, primarily distinguished by the oxidation state of the iron in the heme group, including Methemoglobin (MetHb). Measuring the concentration of MetHb is crucial because it cannot transport oxygen, hence higher concentration of MetHb in the blood causes methemoglobinemia. Here, we use optically detected magnetic relaxometry of paramagnetic iron spins in MetHb drop-casted onto nanostructured diamond doped with shallow high density nitrogen vacancy (NV) spin qubits. We modify the MetHb concentration in the range of 6 x 10^6 - 1.8 x 10^7 adsorbed Fe+3 spins per um^2 and observe an increase of the NV relaxation rate G1 (= 1/T1, T1 is NV spin lattice relaxation time) up to 2 x 10^3 s^-1. NV magnetic relaxometry of MetHb in phosphate-buffered saline solution shows a similar effect with an increase of G1 to 6.7 x 10e3 s^-1 upon increasing the MetHb concentration to 100 uM. The increase of NV G1 is explained by the increased spin noise coming from the Fe+3 spins present in MetHb proteins. This study presents an additional usage of NV quantum sensors to detect paramagnetic centers of biomolecules at volumes below 100 picoliter. △ Less

Submitted 22 October, 2024; v1 submitted 13 May, 2024; originally announced May 2024.

Comments: arXiv admin note: text overlap with arXiv:2310.08605

Journal ref: Appl. Phys. Lett. 125, 114002 (2024)

Deep-learning design of graphene metasurfaces for quantum control and Dirac electron holography

Authors: Chen-Di Han, Li-Li Ye, Zin Lin, Vassilios Kovanis, Ying-Cheng Lai

Abstract: Metasurfaces are sub-wavelength patterned layers for controlling waves in physical systems. In optics, meta-surfaces are created by materials with different dielectric constants and are capable of unconventional functionalities. We develop a deep-learning framework for Dirac-material metasurface design for controlling electronic waves. The metasurface is a configuration of circular graphene quantu… ▽ More Metasurfaces are sub-wavelength patterned layers for controlling waves in physical systems. In optics, meta-surfaces are created by materials with different dielectric constants and are capable of unconventional functionalities. We develop a deep-learning framework for Dirac-material metasurface design for controlling electronic waves. The metasurface is a configuration of circular graphene quantum dots, each created by an electric potential. Employing deep convolutional neural networks, we show that the original scattering wave can be reconstructed with fidelity over 95$\%$, suggesting the feasibility of Dirac electron holography. Additional applications such as plane wave generation, designing broadband, and multi-functionality graphene metasurface systems are illustrated. △ Less

Submitted 1 May, 2024; originally announced May 2024.

Comments: 13 pages, 9 figures

In-situ Doppler-free spectroscopy with pulsed optical fields

Authors: Yuxin Wang, Zhiyue Zheng, Qiuxin Zhang, Yonglang Lai, Zongqi Ge, Tianyi Wang, Liangyu Ding, Smirnov Vasilii, Ilya Semerikov, Shuaining Zhang, Wei Zhang, Xiang Zhang

Abstract: We propose a novel pulsed optical field method that alternately switches the pump beam in conventional saturation absorption to time-division multiplex the same probe beam into both probe and reference beams, followed by digital differential processing to achieve deterministic zero-background Doppler-free spectroscopy. This method effectively mitigates Doppler broadening and common-mode optical no… ▽ More We propose a novel pulsed optical field method that alternately switches the pump beam in conventional saturation absorption to time-division multiplex the same probe beam into both probe and reference beams, followed by digital differential processing to achieve deterministic zero-background Doppler-free spectroscopy. This method effectively mitigates Doppler broadening and common-mode optical noise by addressing disturbances such as non-uniform background absorption and environmental noise, thereby offering enhanced accuracy and robustness. Using this technique, we measured the absolute frequency of Yb$^{+}$ isotopes in the $6s^2\ ^{1}S_0\to 6s6p ^{1}P_1$ transition. By employing an error signal derived from the first-derivative demodulated spectrum of $^{174}\mathrm{Yb}^{+}$, we achieved efficient stabilization of a 369.5 nm ultraviolet diode laser, demonstrating a frequency stability of $3 \times 10^{-11}$ over a 1500-second averaging period and a locking point uncertainty of 850 kHz sustained over 10 days. Furthermore, we report the first in-situ observation of Doppler-free Zeeman sub-level spectra, highlighting the precision of this method and its potential application in measuring magnetic field gradients. △ Less

Submitted 16 February, 2025; v1 submitted 23 April, 2024; originally announced April 2024.

Comments: 6 pages, 6 figures

The Neural Network First-Level Hardware Track Trigger of the Belle II Experiment

Authors: S. Bähr, H. Bae, J. Becker, M. Bertemes, M. Campajola, T. Ferber, G. Inguglia, Y. Iwasaki, T. Jülg, C. Kiesling, Y. -T. Lai, Y. Liu, A. Knoll, T. Koga, A. Lenz, F. Meggendorfer, H. Nakazawa, M. Neu, J. Schieck, E. Schmidt, J. -G. Shiu, S. Skambraks, K. Unger, J. Yin

Abstract: We describe the principles and performance of the first-level ("L1") hardware track trigger of Belle II, based on neural networks. The networks use as input the results from the standard Belle II trigger, which provides "2D" track candidates in the plane transverse to the electron-positron beams. The networks then provide estimates for the origin of the 2D track candidates in direction of the coll… ▽ More We describe the principles and performance of the first-level ("L1") hardware track trigger of Belle II, based on neural networks. The networks use as input the results from the standard Belle II trigger, which provides "2D" track candidates in the plane transverse to the electron-positron beams. The networks then provide estimates for the origin of the 2D track candidates in direction of the colliding beams ("$z$-vertex"), as well as their polar emission angles $θ$. Given the $z$-vertices of the "neural" tracks allows identifying events coming from the collision region ($z \approx 0$), and suppressing the overwhelming background from outside by a suitable cut $d$. Requiring $|z| < d$ for at least one neural track in an event with two or more 2D candidates will set an L1 trigger. The networks also enable a minimum bias trigger, requiring a single 2D track candidate validated by a neural track with a momentum larger than 0.7 GeV in addition to the $|z|$ condition. The momentum of the neural track is derived with the help of the polar angle $θ$. △ Less

Submitted 12 June, 2024; v1 submitted 22 February, 2024; originally announced February 2024.

Comments: 27 pages, 14 figures, to be submittes to NIMA

Machine-learning prediction of tipping with applications to the Atlantic Meridional Overturning Circulation

Authors: Shirin Panahi, Ling-Wei Kong, Mohammadamin Moradi, Zheng-Meng Zhai, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai

Abstract: Anticipating a tipping point, a transition from one stable steady state to another, is a problem of broad relevance due to the ubiquity of the phenomenon in diverse fields. The steady-state nature of the dynamics about a tipping point makes its prediction significantly more challenging than predicting other types of critical transitions from oscillatory or chaotic dynamics. Exploiting the benefits… ▽ More Anticipating a tipping point, a transition from one stable steady state to another, is a problem of broad relevance due to the ubiquity of the phenomenon in diverse fields. The steady-state nature of the dynamics about a tipping point makes its prediction significantly more challenging than predicting other types of critical transitions from oscillatory or chaotic dynamics. Exploiting the benefits of noise, we develop a general data-driven and machine-learning approach to predicting potential future tipping in nonautonomous dynamical systems and validate the framework using examples from different fields. As an application, we address the problem of predicting the potential collapse of the Atlantic Meridional Overturning Circulation (AMOC), possibly driven by climate-induced changes in the freshwater input to the North Atlantic. Our predictions based on synthetic and currently available empirical data place a potential collapse window spanning from 2040 to 2065, in consistency with the results in the current literature. △ Less

Submitted 17 October, 2024; v1 submitted 21 February, 2024; originally announced February 2024.

Comments: 11 pages, 7 figures

Spin-dependent edge states in two-dimensional Dirac materials with a flat band

Authors: Li-Li Ye, Chen-Di Han, Ying-Cheng Lai

Abstract: The phenomenon of spin-dependent quantum scattering in two-dimensional (2D) pseudospin-1/2 Dirac materials leading to a relativistic quantum chimera was recently uncovered. We investigate spin-dependent Dirac electron optics in 2D pseudospin-1 Dirac materials, where the energy-band structure consists of a pair of Dirac cones and a flat band. In particular, with a suitable combination of external e… ▽ More The phenomenon of spin-dependent quantum scattering in two-dimensional (2D) pseudospin-1/2 Dirac materials leading to a relativistic quantum chimera was recently uncovered. We investigate spin-dependent Dirac electron optics in 2D pseudospin-1 Dirac materials, where the energy-band structure consists of a pair of Dirac cones and a flat band. In particular, with a suitable combination of external electric fields and a magnetic exchange field, electrons with a specific spin orientation (e.g., spin-down) can be trapped in a class of long-lived edge modes, generating resonant scattering. The spin-dependent edge states are a unique feature of flat-band Dirac materials and have no classical correspondence. However, electrons with the opposite spin (i.e., spin up) undergo conventional quantum scattering with a classical correspondence, which can be understood in the framework of Dirac electron optics. A consequence is that the spin-down electrons produce a large scattering probability with broad scattering angle distribution in both near- and far-field regions, while the spin-up electrons display the opposite behavior. Such characteristically different behaviors of the electrons with opposite spins lead to spin polarization that can be as high as nearly 100%. △ Less

Submitted 21 August, 2024; v1 submitted 21 February, 2024; originally announced February 2024.

Comments: 18 pages, 9 figures, journal

Journal ref: Phys. Rev. B 108 (2023), 235404

Optical properties of two dimensional Dirac Weyl materials with a flatband

Authors: Li-Li Ye, Chen-Di Han, Ying-Cheng Lai

Abstract: The emergence of a flat band in Dirac-Weyl materials offers new possibilities for electronic transitions, leading to stronger interaction with light. As a result, the optical conductivity can be significantly enhanced in these flat-band materials as compared with graphene, making them potentially better candidates for optical sensing and modulation. Recently, a comprehensive theory for the optical… ▽ More The emergence of a flat band in Dirac-Weyl materials offers new possibilities for electronic transitions, leading to stronger interaction with light. As a result, the optical conductivity can be significantly enhanced in these flat-band materials as compared with graphene, making them potentially better candidates for optical sensing and modulation. Recently, a comprehensive theory for the optical conductivity of a spectrum of flat-band Dirac-Weyl materials has been developed, with explicit formulas for both the real and imaginary parts of the conductivity derived through two independent approaches. This Perspective offers a review of the development. An understanding of the optical properties of the flat-band Dirac-Weyl materials paves the way for optical device applications in the terahertz-frequency domain. △ Less

Submitted 21 February, 2024; originally announced February 2024.

Comments: 8 pages, 3 figures, journal

Journal ref: Appl. Phys. Lett. 124, 060501 (2024)

Random forests for detecting weak signals and extracting physical information: a case study of magnetic navigation

Authors: Mohammadamin Moradi, Zheng-Meng Zhai, Aaron Nielsen, Ying-Cheng Lai

Abstract: It was recently demonstrated that two machine-learning architectures, reservoir computing and time-delayed feed-forward neural networks, can be exploited for detecting the Earth's anomaly magnetic field immersed in overwhelming complex signals for magnetic navigation in a GPS-denied environment. The accuracy of the detected anomaly field corresponds to a positioning accuracy in the range of 10 to… ▽ More It was recently demonstrated that two machine-learning architectures, reservoir computing and time-delayed feed-forward neural networks, can be exploited for detecting the Earth's anomaly magnetic field immersed in overwhelming complex signals for magnetic navigation in a GPS-denied environment. The accuracy of the detected anomaly field corresponds to a positioning accuracy in the range of 10 to 40 meters. To increase the accuracy and reduce the uncertainty of weak signal detection as well as to directly obtain the position information, we exploit the machine-learning model of random forests that combines the output of multiple decision trees to give optimal values of the physical quantities of interest. In particular, from time-series data gathered from the cockpit of a flying airplane during various maneuvering stages, where strong background complex signals are caused by other elements of the Earth's magnetic field and the fields produced by the electronic systems in the cockpit, we demonstrate that the random-forest algorithm performs remarkably well in detecting the weak anomaly field and in filtering the position of the aircraft. With the aid of the conventional inertial navigation system, the positioning error can be reduced to less than 10 meters. We also find that, contrary to the conventional wisdom, the classic Tolles-Lawson model for calibrating and removing the magnetic field generated by the body of the aircraft is not necessary and may even be detrimental for the success of the random-forest method. △ Less

Submitted 21 February, 2024; originally announced February 2024.

Comments: 12 pages, 11 figures

Journal ref: APL Machine Learning 2 (1), 016118 (2024)

Machine-learning parameter tracking with partial state observation

Authors: Zheng-Meng Zhai, Mohammadamin Moradi, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai

Abstract: Complex and nonlinear dynamical systems often involve parameters that change with time, accurate tracking of which is essential to tasks such as state estimation, prediction, and control. Existing machine-learning methods require full state observation of the underlying system and tacitly assume adiabatic changes in the parameter. Formulating an inverse problem and exploiting reservoir computing,… ▽ More Complex and nonlinear dynamical systems often involve parameters that change with time, accurate tracking of which is essential to tasks such as state estimation, prediction, and control. Existing machine-learning methods require full state observation of the underlying system and tacitly assume adiabatic changes in the parameter. Formulating an inverse problem and exploiting reservoir computing, we develop a model-free and fully data-driven framework to accurately track time-varying parameters from partial state observation in real time. In particular, with training data from a subset of the dynamical variables of the system for a small number of known parameter values, the framework is able to accurately predict the parameter variations in time. Low- and high-dimensional, Markovian and non-Markovian nonlinear dynamical systems are used to demonstrate the power of the machine-learning based parameter-tracking framework. Pertinent issues affecting the tracking performance are addressed. △ Less

Submitted 15 November, 2023; originally announced November 2023.

Comments: 5 pages, 4 figures