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The SuperCLUE-Fin (SC-Fin) benchmark is a pioneering evaluation framework tailored for Chinese-native financial large language models (FLMs). It assesses FLMs across six financial application domains and twenty-five specialized tasks, encompassing theoretical knowledge and practical applications such as compliance, risk management, and investment analysis. Using multi-turn, open-ended conversations that mimic real-life scenarios, SC-Fin measures models on a range of criteria, including accurate financial understanding, logical reasoning, clarity, computational efficiency, business acumen, risk perception, and compliance with Chinese regulations. In a rigorous evaluation involving over a thousand questions, SC-Fin identifies a performance hierarchy where domestic models like GLM-4 and MoonShot-v1-128k outperform others with an A-grade, highlighting the potential for further development in transforming theoretical knowledge into pragmatic financial solutions. This benchmark serves as a critical tool for refining FLMs in the Chinese context, directing improvements in financial knowledge databases, standardizing financial interpretations, and promoting models that prioritize compliance, risk management, and secure practices. We create a contextually relevant and comprehensive benchmark that drives the development of AI in the Chinese financial sector. SC-Fin facilitates the advancement and responsible deployment of FLMs, offering valuable insights for enhancing model performance and usability for both individual and institutional users in the Chinese market..~\footnote{Our benchmark can be found at \url{https://www.CLUEbenchmarks.com}}., Comment: 11 pages, 19 figures, and tables

Traditional spectral imaging methods are constrained by the time-consuming scanning process, limiting the application in dynamic scenarios. One-shot spectral imaging based on reconstruction has been a hot research topic recently and the primary challenges still lie in both efficient fabrication techniques suitable for mass production and the high-speed, high-accuracy reconstruction algorithm for real-time spectral imaging. In this study, we introduce an innovative on-chip real-time hyperspectral imager that leverages nanophotonic film spectral encoders and a Massively Parallel Network (MP-Net), featuring a 4 * 4 array of compact, all-dielectric film units for the micro-spectrometers. Each curved nanophotonic film unit uniquely modulates incident light across the underlying 3 * 3 CMOS image sensor (CIS) pixels, enabling a high spatial resolution equivalent to the full CMOS resolution. The implementation of MP-Net, specially designed to address variability in transmittance and manufacturing errors such as misalignment and non-uniformities in thin film deposition, can greatly increase the structural tolerance of the device and reduce the preparation requirement, further simplifying the manufacturing process. Tested in varied environments on both static and moving objects, the real-time hyperspectral imager demonstrates the robustness and high-fidelity spatial-spectral data capabilities across diverse scenarios. This on-chip hyperspectral imager represents a significant advancement in real-time, high-resolution spectral imaging, offering a versatile solution for applications ranging from environmental monitoring, remote sensing to consumer electronics.

We revisit the masses of heavy quarkonium double-gluon hybrid mesons with exotic quantum numbers $J^{PC}=1^{-+}$ and $2^{+-}$ in the framework of the QCD sum rules. Considering the double-gluon hybrid meson operators in the octet-octet color structure, we have constructed two independent interpolating currents with $J^{PC}=1^{-+}$ and five independent currents with $J^{PC}=2^{+-}$. For the interpolating currents with antisymmetric glueball operator, there exist non-local divergences in one kind of additional Feynman diagrams of tri-gluon condensate, which will give important contributions to the sum rule stabilities and mass predictions. We use the diagrammatic operator renormalization to cancel out such divergences. At the leading order of $\alpha_s$, the two-point correlation functions and spectral densities can be expressed in the analytic form of the generalized hypergeometric functions and Meijer's G-functions. After performing the numerical analysis, we predict the masses of the $1^{-+}$ and $2^{+-}$ charmonium double-gluon hybrid mesons to be around $6.1-7.2$ GeV and $6.3-6.4$ GeV, respectively. For the bottomonium systems, their masses are predicted to be $13.7-14.3$ GeV and $12.6-13.3$ GeV for the $1^{-+}$ and $2^{+-}$ channels, respectively. Besides, it is possible to hunt for these charmonium hybrids in the radiative decays of bottomonium mesons in BelleII experiment. Further investigations on these hybrid states in various theoretical and phenomenological methods are also anticipated in the future., Comment: 34 pages, 6 figures

This paper proposes a novel learning-based approach for achieving exponential stabilization of nonlinear control-affine systems. We leverage the Control Contraction Metrics (CCMs) framework to co-synthesize Neural Contraction Metrics (NCMs) and Neural Network (NN) controllers. First, we transform the infinite-dimensional semi-definite program (SDP) for CCM computation into a tractable inequality feasibility problem using element-wise bounds of matrix-valued functions. The terms in the inequality can be efficiently computed by our novel algorithms. Second, we propose a free parametrization of NCMs guaranteeing positive definiteness and the satisfaction of a partial differential equation, regardless of trainable parameters. Third, this parametrization and the inequality condition enable the design of contractivity-enforcing regularizers, which can be incorporated while designing the NN controller for exponential stabilization of the underlying nonlinear systems. Furthermore, when the training loss goes to zero, we provide formal guarantees on verification of the NCM and the exponentional stabilization under the NN controller. Finally, we validate our method through benchmark experiments on set-point stabilization and increasing the region of attraction of a locally pre-stabilized closed-loop system., Comment: This paper is submitted in CDC2024 for a possible publication

Humans constantly interact with their surrounding environments. Current human-centric generative models mainly focus on synthesizing humans plausibly interacting with static scenes and objects, while the dynamic human action-reaction synthesis for ubiquitous causal human-human interactions is less explored. Human-human interactions can be regarded as asymmetric with actors and reactors in atomic interaction periods. In this paper, we comprehensively analyze the asymmetric, dynamic, synchronous, and detailed nature of human-human interactions and propose the first multi-setting human action-reaction synthesis benchmark to generate human reactions conditioned on given human actions. To begin with, we propose to annotate the actor-reactor order of the interaction sequences for the NTU120, InterHuman, and Chi3D datasets. Based on them, a diffusion-based generative model with a Transformer decoder architecture called ReGenNet together with an explicit distance-based interaction loss is proposed to predict human reactions in an online manner, where the future states of actors are unavailable to reactors. Quantitative and qualitative results show that our method can generate instant and plausible human reactions compared to the baselines, and can generalize to unseen actor motions and viewpoint changes., Comment: Accepted by CVPR 2024, Project Page: https://liangxuy.github.io/ReGenNet/

In the context of online education, designing an automatic solver for geometric problems has been considered a crucial step towards general math Artificial Intelligence (AI), empowered by natural language understanding and traditional logical inference. In most instances, problems are addressed by adding auxiliary components such as lines or points. However, adding auxiliary components automatically is challenging due to the complexity in selecting suitable auxiliary components especially when pivotal decisions have to be made. The state-of-the-art performance has been achieved by exhausting all possible strategies from the category library to identify the one with the maximum likelihood. However, an extensive strategy search have to be applied to trade accuracy for ef-ficiency. To add auxiliary components automatically and efficiently, we present deep reinforcement learning framework based on the language model, such as BERT. We firstly apply the graph attention mechanism to reduce the strategy searching space, called AttnStrategy, which only focus on the conclusion-related components. Meanwhile, a novel algorithm, named Automatically Adding Auxiliary Components using Reinforcement Learning framework (A3C-RL), is proposed by forcing an agent to select top strategies, which incorporates the AttnStrategy and BERT as the memory components. Results from extensive experiments show that the proposed A3C-RL algorithm can substantially enhance the average precision by 32.7% compared to the traditional MCTS. In addition, the A3C-RL algorithm outperforms humans on the geometric questions from the annual University Entrance Mathematical Examination of China.

Deep neural networks (DNN) are increasingly being used to learn controllers due to their excellent approximation capabilities. However, their black-box nature poses significant challenges to closed-loop stability guarantees and performance analysis. In this paper, we introduce a structured DNN-based controller for the trajectory tracking control of Lagrangian systems using backing techniques. By properly designing neural network structures, the proposed controller can ensure closed-loop stability for any compatible neural network parameters. In addition, improved control performance can be achieved by further optimizing neural network parameters. Besides, we provide explicit upper bounds on tracking errors in terms of controller parameters, which allows us to achieve the desired tracking performance by properly selecting the controller parameters. Furthermore, when system models are unknown, we propose an improved Lagrangian neural network (LNN) structure to learn the system dynamics and design the controller. We show that in the presence of model approximation errors and external disturbances, the closed-loop stability and tracking control performance can still be guaranteed. The effectiveness of the proposed approach is demonstrated through simulations.

Narrow-line Seyfert 1 galaxies (NLS1s), a subclass of active galactic nuclei (AGNs) at early stage of accretion process, are also found to host relativistic jets. However, currently known jetted NLS1s are rare. The majority of NLS1s are undetected at radio band. The radio detection rate of NLS1s raises with the LOFAR Two-metre Sky Survey (LoTSS), which gives a good opportunity to find more jetted NLS1s. The better sensitivity brings another question of whether the radio emission of NLS1s with low radio luminosity originates from jet activity. In order to clarify the origin of radio emission for NLS1s, and search for more jetted NLS1s, we explore the mid-infrared properties of LoTSS detected NLS1s by comparing them with known jetted AGNs and star forming galaxies (SFGs), which locate above and on the well studied radio/far-infrared correlation, respectively. The majority of NLS1s show mid-infrared (MIR) excess compared with SFGs. Their radio emission shows significant correlation with MIR emission. In MIR color-color diagram, NLS1s are overlapped with flat spectrum radio quasars, but well separated from SFGs and optically selected radio galaxies. The flux ratio between radio and MIR emission of these NLS1s is also similar with a radio quiet quasar with weak jet. These results imply substantial contributions from AGN activities for both radio and MIR emission of NLS1s. A small fraction of NLS1s with relatively higher radio luminosity locate in similar region with blazars in radio-MIR diagram, which suggests that the radio emission of these NLS1s is dominated by jet. We obtain a sample of jetted NLS1 candidates through their radio excess in radio-MIR diagram., Comment: Accepted for Publication in ApJ

We have studied the mass spectra of the P-wave fully charm and fully bottom tetraquark states in the framework of QCD sum rules. We construct the interpolating currents by inserting the covariant derivative operator $\overset{ \leftrightarrow } { \mathcal D }_{ \mu }$ between the S-wave diquark and antidiquark fields. The excitation structures show that the pure $\lambda$-mode excited P-wave fully heavy tetraquarks exist for the quantum numbers $J^{PC}=1^{--}, 1^{-+}, 2^{--}, 2^{-+}$ and $3^{--}$, while it is difficult to separate the $\lambda$-mode and $\rho$-mode excitations in the $0^{-+}$ channel. Within three Lorentz indices, there is no pure $\lambda$-mode excited P-wave fully charm/bottom tetraquark operators with $J^{PC}=0^{--}$ and $3^{-+}$. Our results support that the recent observed $X(6900)$ and $X(7200)$ resonances could be interpreted as the P-wave fully charm $cc \bar c \bar c$ tetraquark states with $J^{PC}=1^{-+}$ and $2^{-+}$, respectively. Some P-wave fully bottom $bb\bar b\bar b$ tetraquark states are predicted to be lower than the di-$\eta_b(1S)$ and di-$\Upsilon(1S)$ mass thresholds. Hopefully our calculations will be useful for identifying the nature of new exotic tetraquark states., Comment: 18 pages, 11 figures. Accepted by Physical Review D

We introduce SuperCLUE-Math6(SC-Math6), a new benchmark dataset to evaluate the mathematical reasoning abilities of Chinese language models. SC-Math6 is designed as an upgraded Chinese version of the GSM8K dataset with enhanced difficulty, diversity, and application scope. It consists of over 2000 mathematical word problems requiring multi-step reasoning and providing natural language solutions. We propose an innovative scheme to quantify the reasoning capability of large models based on performance over problems with different reasoning steps. Experiments on 13 representative Chinese models demonstrate a clear stratification of reasoning levels, with top models like GPT-4 showing superior performance. SC-Math6 fills the gap in Chinese mathematical reasoning benchmarks and provides a comprehensive testbed to advance the intelligence of Chinese language models., Comment: Dataset revised and finalized, results updated with new model; 8 pages, 7 figures, 4 tables

We have studied the mass spectra of doubly charmed pentaquark states in the $\Lambda _{c}^{(*)}D^{(*)}$ and $\Sigma _{c}^{(*)}D^{(*)}$ channels with $J^P=1/2^\pm$, $3/2^\pm$ and $5/2^\pm$ within the framework of QCD sum rules. We use the parity projected sum rules to separate the contributions of negative and positive parities from the two-point correlations induced by the pentaquark interpolating currents. Our results show that the bound states of $P_{cc}$ pentaquarks may exist in the $\Lambda _cD\, (\frac{1}{2}^-)$, $\Sigma _cD\, (\frac{1}{2}^-)$, $\Sigma _cD^*\, (\frac{3}{2}^-)$, $\Lambda _c^*D\, (\frac{3}{2}^-)$, $\Lambda _c^*D^*\, (\frac{5}{2}^-)$ channels with negative-parity and $\Sigma _cD\, (\frac{1}{2}^+)$, $\Sigma _cD^\ast\, (\frac{3}{2}^+)$, $\Sigma _c^\ast D\, (\frac{3}{2}^+)$ channels with positive-parity, since their masses are predicted to be lower than the corresponding meson-baryon thresholds. However, they are still allowed to decay into the $\Xi_{cc}^{(\ast)}\pi$ final states via strong interaction. The triply charged $P_{cc}^{+++}(ccuu\bar d)$ and neutral $P_{cc}^{0}(ccdd\bar u)$ in the isospin quartet would definitely be pentaquark states due to their exotic charges. We suggest searching for these characteristic doubly charmed pentaquark signals in the $P_{cc}^{+++}\to\Xi_{cc}^{(\ast) ++}\pi^+/\rho^+$, $\Sigma_c^{(\ast)++}D^{(\ast)+}$ and $P_{cc}^{0}\to\Xi_{cc}^{(\ast) +}\pi^-/\rho^-$, $\Sigma_c^{(\ast)0}D^{(\ast)0}$ decays in the near future., Comment: 25 pages, 5 figures. Accepted by Physical Review D

The analysis of the ubiquitous human-human interactions is pivotal for understanding humans as social beings. Existing human-human interaction datasets typically suffer from inaccurate body motions, lack of hand gestures and fine-grained textual descriptions. To better perceive and generate human-human interactions, we propose Inter-X, a currently largest human-human interaction dataset with accurate body movements and diverse interaction patterns, together with detailed hand gestures. The dataset includes ~11K interaction sequences and more than 8.1M frames. We also equip Inter-X with versatile annotations of more than 34K fine-grained human part-level textual descriptions, semantic interaction categories, interaction order, and the relationship and personality of the subjects. Based on the elaborate annotations, we propose a unified benchmark composed of 4 categories of downstream tasks from both the perceptual and generative directions. Extensive experiments and comprehensive analysis show that Inter-X serves as a testbed for promoting the development of versatile human-human interaction analysis. Our dataset and benchmark will be publicly available for research purposes., Comment: Project page: https://liangxuy.github.io/inter-x/

Improving three-dimensional (3D) localization precision is of paramount importance for super-resolution imaging. By properly engineering the point spread function (PSF), such as utilizing Laguerre-Gaussian (LG) modes and their superposition, the ultimate limits of 3D localization precision can be enhanced. However, achieving these limits is challenging, as it often involves complicated detection strategies and practical limitations. In this work, we rigorously derive the ultimate 3D localization limits of LG modes and their superposition, specifically rotation modes, in the multi-parameter estimation framework. Our findings reveal that a significant portion of the information required for achieving 3D super-localization of LG modes can be obtained through feasible intensity detection. Moreover, the 3D ultimate precision can be achieved when the azimuthal index $l$ is zero. To provide a proof-of-principle demonstration, we develop an iterative maximum likelihood estimation (MLE) algorithm that converges to the 3D position of a point source, considering the pixelation and detector noise. The experimental implementation exhibits an improvement of up to two-fold in lateral localization precision and up to twenty-fold in axial localization precision when using LG modes compared to Gaussian mode. We also showcase the superior axial localization capability of the rotation mode within the near-focus region, effectively overcoming the limitations encountered by single LG modes. Notably, in the presence of realistic aberration, the algorithm robustly achieves the Cram\'{e}r-Rao lower bound. Our findings provide valuable insights for evaluating and optimizing the achievable 3D localization precision, which will facilitate the advancements in super-resolution microscopy.

The shock strength dependent formation of diamond represents one of the most intriguing questions in graphite research. Using ab initio DFT-trained carbon GNN model, we observe a strength-dependent graphite transition under shock. The poor sliding caused by scarce sliding time under high-strength shock forms hexagonal diamond with an orientation of (001)G//(100)HD+[010]G//[010]HD; under low-strength shock, cubic diamond forms after enough sliding time, unveiling the strength-dependent graphite transition. We provide computational evidence of the strength-dependent graphite transition from first principles, clarifying the long-term shock-induced hexagonal formation and structural strength-dependent trend source.

Large language models (LLMs), like ChatGPT and GPT-4, have demonstrated remarkable abilities in natural language understanding and generation. However, alongside their positive impact on our daily tasks, they can also produce harmful content that negatively affects societal perceptions. To systematically assess the safety of Chinese LLMs, we introduce SuperCLUE-Safety (SC-Safety) - a multi-round adversarial benchmark with 4912 open-ended questions covering more than 20 safety sub-dimensions. Adversarial human-model interactions and conversations significantly increase the challenges compared to existing methods. Experiments on 13 major LLMs supporting Chinese yield the following insights: 1) Closed-source models outperform open-sourced ones in terms of safety; 2) Models released from China demonstrate comparable safety levels to LLMs like GPT-3.5-turbo; 3) Some smaller models with 6B-13B parameters can compete effectively in terms of safety. By introducing SC-Safety, we aim to promote collaborative efforts to create safer and more trustworthy LLMs. The benchmark and findings provide guidance on model selection. Our benchmark can be found at https://www.CLUEbenchmarks.com, Comment: 20 pages, 8 tables, 16 figures

Blended learning is generally defined as the combination of traditional face-to-face learning and online learning. This learning mode has been widely used in advanced education across the globe due to the COVID-19 pandemic's social distance restriction as well as the development of technology. Online learning plays an important role in blended learning, and as it requires more student autonomy, the quality of blended learning in advanced education has been a persistent concern. Existing literature offers several elements and frameworks regarding evaluating the quality of blended learning. However, most of them either have different favours for evaluation perspectives or simply offer general guidance for evaluation, reducing the completeness, objectivity and practicalness of related works. In order to carry out a more intuitive and comprehensive evaluation framework, this paper proposes a hierarchy-based analysis approach. Applying gradient boosting model and feature importance evaluation method, this approach mainly analyses student engagement and its three identified dimensions (behavioral engagement, emotional engagement, cognitive engagement) to eliminate some existing stubborn problems when it comes to blended learning evaluation. The results show that cognitive engagement and emotional engagement play a more important role in blended learning evaluation, implying that these two should be considered to improve for better learning as well as teaching quality., Comment: Accepted by the 7th APWeb-WAIM International Joint Conference on Web and Big Data. (APWeb 2023)

Video traffic in vehicular communication networks (VCNs) faces exponential growth. However, different segments of most videos reveal various attractiveness for viewers, and the pre-caching decision is greatly affected by the dynamic service duration that edge nodes can provide services for mobile vehicles driving along a road. In this paper, we propose an efficient video highlight pre-caching scheme in the vehicular communication network, adapting to the service duration. Specifically, a highlight entropy model is devised with the consideration of the segments' popularity and continuity between segments within a period of time, based on which, an optimization problem of video highlight pre-caching is formulated. As this problem is non-convex and lacks a closed-form expression of the objective function, we decouple multiple variables by deriving candidate highlight segmentations of videos through wavelet transform, which can significantly reduce the complexity of highlight pre-caching. Then the problem is solved iteratively by a highlight-direction trimming algorithm, which is proven to be locally optimal. Simulation results based on real-world video datasets demonstrate significant improvement in highlight entropy and jitter compared to benchmark schemes.

Game Based Language Learning (GBLL) is an approach to language learning in which learning happens in the process of playing a game. GBLL has several potential benefits including enhanced reading, writing, and cultural awareness. This paper provides a brief overview of Cipher -- Faoi Gheasa, a GBLL app for Irish and learners' feedback on the game. The app inverts the current Intelligent Computer Assisted Language Learning (ICALL) approach to error detection and it asks players (learners) to spot the deliberate errors (ciphers) in texts that have been changed by an evil character called Sypher. Many elements of game playing are included in the app. It has been played by over 150 primary school learners of Irish. While there is still room for improvement, the overall feedback from the students was very positive. [For the complete volume, "Intelligent CALL, Granular Systems and Learner Data: Short Papers from EUROCALL 2022 (30th, Reykjavik, Iceland, August 17-19, 2022)," see ED624779.]

Multimodal data, which can comprehensively perceive and recognize the physical world, has become an essential path towards general artificial intelligence. However, multimodal large models trained on public datasets often underperform in specific industrial domains. This paper proposes a multimodal federated learning framework that enables multiple enterprises to utilize private domain data to collaboratively train large models for vertical domains, achieving intelligent services across scenarios. The authors discuss in-depth the strategic transformation of federated learning in terms of intelligence foundation and objectives in the era of big model, as well as the new challenges faced in heterogeneous data, model aggregation, performance and cost trade-off, data privacy, and incentive mechanism. The paper elaborates a case study of leading enterprises contributing multimodal data and expert knowledge to city safety operation management , including distributed deployment and efficient coordination of the federated learning platform, technical innovations on data quality improvement based on large model capabilities and efficient joint fine-tuning approaches. Preliminary experiments show that enterprises can enhance and accumulate intelligent capabilities through multimodal model federated learning, thereby jointly creating an smart city model that provides high-quality intelligent services covering energy infrastructure safety, residential community security, and urban operation management. The established federated learning cooperation ecosystem is expected to further aggregate industry, academia, and research resources, realize large models in multiple vertical domains, and promote the large-scale industrial application of artificial intelligence and cutting-edge research on multimodal federated learning.

Large language models (LLMs) have shown the potential to be integrated into human daily lives. Therefore, user preference is the most critical criterion for assessing LLMs' performance in real-world scenarios. However, existing benchmarks mainly focus on measuring models' accuracy using multi-choice questions, which limits the understanding of their capabilities in real applications. We fill this gap by proposing a comprehensive Chinese benchmark SuperCLUE, named after another popular Chinese LLM benchmark CLUE. SuperCLUE encompasses three sub-tasks: actual users' queries and ratings derived from an LLM battle platform (CArena), open-ended questions with single and multiple-turn dialogues (OPEN), and closed-ended questions with the same stems as open-ended single-turn ones (CLOSE). Our study shows that accuracy on closed-ended questions is insufficient to reflect human preferences achieved on open-ended ones. At the same time, they can complement each other to predict actual user preferences. We also demonstrate that GPT-4 is a reliable judge to automatically evaluate human preferences on open-ended questions in a Chinese context. Our benchmark will be released at https://www.CLUEbenchmarks.com, Comment: 13 pages, 12 figures, 5 tables

The UNet architecture, based on Convolutional Neural Networks (CNN), has demonstrated its remarkable performance in medical image analysis. However, it faces challenges in capturing long-range dependencies due to the limited receptive fields and inherent bias of convolutional operations. Recently, numerous transformer-based techniques have been incorporated into the UNet architecture to overcome this limitation by effectively capturing global feature correlations. However, the integration of the Transformer modules may result in the loss of local contextual information during the global feature fusion process. To overcome these challenges, we propose a 2D medical image segmentation model called Multi-scale Cross Perceptron Attention Network (MCPA). The MCPA consists of three main components: an encoder, a decoder, and a Cross Perceptron. The Cross Perceptron first captures the local correlations using multiple Multi-scale Cross Perceptron modules, facilitating the fusion of features across scales. The resulting multi-scale feature vectors are then spatially unfolded, concatenated, and fed through a Global Perceptron module to model global dependencies. Furthermore, we introduce a Progressive Dual-branch Structure to address the semantic segmentation of the image involving finer tissue structures. This structure gradually shifts the segmentation focus of MCPA network training from large-scale structural features to more sophisticated pixel-level features. We evaluate our proposed MCPA model on several publicly available medical image datasets from different tasks and devices, including the open large-scale dataset of CT (Synapse), MRI (ACDC), fundus camera (DRIVE, CHASE_DB1, HRF), and OCTA (ROSE). The experimental results show that our MCPA model achieves state-of-the-art performance. The code is available at https://github.com/simonustc/MCPA-for-2D-Medical-Image-Segmentation.

We propose a novel learning-based surrogate data assimilation (DA) model for efficient state estimation in a limited area. Our model employs a feedforward neural network for online computation, eliminating the need for integrating high-dimensional limited-area models. This approach offers significant computational advantages over traditional DA algorithms. Furthermore, our method avoids the requirement of lateral boundary conditions for the limited-area model in both online and offline computations. The design of our surrogate DA model is built upon a robust theoretical framework that leverages two fundamental concepts: observability and effective region. The concept of observability enables us to quantitatively determine the optimal amount of observation data necessary for accurate DA. Meanwhile, the concept of effective region substantially reduces the computational burden associated with computing observability and generating training data.