arXiv subjects

Bo Shen

Publications and source records attributed to Bo Shen.

At least 37 records · Page 2Linked to original sources

arXiv2025

Comparing LSTM-Based Sequence-to-Sequence Forecasting Strategies for 24-Hour Solar Proton Flux Profiles Using GOES Data

Solar Proton Events (SPEs) cause significant radiation hazards to satellites, astronauts, and technological systems. Accurate forecasting of their proton flux time profiles is crucial for early warnings and mitigation. This paper explores deep learning sequence-to-sequence (seq2seq) models based on Long Short-Term Memory networks to predict 24-hour proton flux profiles following SPE onsets. We used a dataset of 40 well-connected SPEs (1997-2017) observed by NOAA GOES, each associated with a >=M-class western-hemisphere solar flare and undisturbed proton flux profiles. Using 4-fold stratified cross-validation, we evaluate seq2seq model configurations (varying hidden units and embedding dimensions) under multiple forecasting scenarios: (i) proton-only input vs. combined proton+X-ray input, (ii) original flux data vs. trend-smoothed data, and (iii) autoregressive vs. one-shot forecasting. Our major results are as follows: First, one-shot forecasting consistently yields lower error than autoregressive prediction, avoiding the error accumulation seen in iterative approaches. Second, on the original data, proton-only models outperform proton+X-ray models. However, with trend-smoothed data, this gap narrows or reverses in proton+X-ray models. Third, trend-smoothing significantly enhances the performance of proton+X-ray models by mitigating fluctuations in the X-ray channel. Fourth, while models trained on trendsmoothed data perform best on average, the best-performing model was trained on original data, suggesting that architectural choices can sometimes outweigh the benefits of data preprocessing.

cs.LG↗

arXiv2025

Improving the Spatial Resolution of GONG Solar Images to GST Quality Using Deep Learning

High-resolution (HR) solar imaging is crucial for capturing fine-scale dynamic features such as filaments and fibrils. However, the spatial resolution of the full-disk H$α$ images is limited and insufficient to resolve these small-scale structures. To address this, we propose a GAN-based superresolution approach to enhance low-resolution (LR) full-disk H$α$ images from the Global Oscillation Network Group (GONG) to a quality comparable with HR observations from the Big Bear Solar Observatory/Goode Solar Telescope (BBSO/GST). We employ Real-ESRGAN with Residual-in-Residual Dense Blocks and a relativistic discriminator. We carefully aligned GONG-GST pairs. The model effectively recovers fine details within sunspot penumbrae and resolves fine details in filaments and fibrils, achieving an average mean squared error (MSE) of 467.15, root mean squared error (RMSE) of 21.59, and cross-correlation (CC) of 0.7794. Slight misalignments between image pairs limit quantitative performance, which we plan to address in future work alongside dataset expansion to further improve reconstruction quality.

cs.CV↗

arXiv2025

EfficientEdit: Accelerating Code Editing via Edit-Oriented Speculative Decoding

Large Language Models (LLMs) have demonstrated remarkable capabilities in code editing, substantially enhancing software development productivity. However, the inherent complexity of code editing tasks forces existing approaches to rely on LLMs' autoregressive end-to-end generation, where decoding speed plays a critical role in efficiency. While inference acceleration techniques like speculative decoding are applied to improve the decoding efficiency, these methods fail to account for the unique characteristics of code editing tasks where changes are typically localized and existing code segments are reused. To address this limitation, we propose EfficientEdit, a novel method that improves LLM-based code editing efficiency through two key mechanisms based on speculative decoding: (1) effective reuse of original code segments while identifying potential edit locations, and (2) efficient generate edit content via high-quality drafts from edit-oriented draft models and a dynamic verification mechanism that balances quality and acceleration. Experimental results show that EfficientEdit can achieve up to 10.38$\times$ and 13.09$\times$ speedup compared to standard autoregressive decoding in CanItEdit and CodeIF-Bench, respectively, outperforming state-of-the-art inference acceleration approaches by up to 90.6%. The code and data are available at https://github.com/zhu-zhu-ding/EfficientEdit.

cs.SE↗

arXiv2025

Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering

Linear concept vectors effectively steer LLMs, but existing methods suffer from noisy features in diverse datasets that undermine steering robustness. We propose Sparse Autoencoder-Denoised Concept Vectors (SDCV), which selectively keep the most discriminative SAE latents while reconstructing hidden representations. Our key insight is that concept-relevant signals can be explicitly separated from dataset noise by scaling up activations of top-k latents that best differentiate positive and negative samples. Applied to linear probing and difference-in-mean, SDCV consistently improves steering success rates by 4-16\% across six challenging concepts, while maintaining topic relevance.

cs.CL↗

arXiv2025

Causality-informed Anomaly Detection in Partially Observable Sensor Networks: Moving beyond Correlations

Nowadays, as AI-driven manufacturing becomes increasingly popular, the volume of data streams requiring real-time monitoring continues to grow. However, due to limited resources, it is impractical to place sensors at every location to detect unexpected shifts. Therefore, it is necessary to develop an optimal sensor placement strategy that enables partial observability of the system while detecting anomalies as quickly as possible. Numerous approaches have been proposed to address this challenge; however, most existing methods consider only variable correlations and neglect a crucial factor: Causality. Moreover, although a few techniques incorporate causal analysis, they rely on interventions-artificially creating anomalies-to identify causal effects, which is impractical and might lead to catastrophic losses. In this paper, we introduce a causality-informed deep Q-network (Causal DQ) approach for partially observable sensor placement in anomaly detection. By integrating causal information at each stage of Q-network training, our method achieves faster convergence and tighter theoretical error bounds. Furthermore, the trained causal-informed Q-network significantly reduces the detection time for anomalies under various settings, demonstrating its effectiveness for sensor placement in large-scale, real-world data streams. Beyond the current implementation, our technique's fundamental insights can be applied to various reinforcement learning problems, opening up new possibilities for real-world causality-informed machine learning methods in engineering applications.

cs.AI↗

arXiv2025

MVPinn: Integrating Milne-Eddington Inversion with Physics-Informed Neural Networks for GST/NIRIS Observations

We introduce MVPinn, a Physics-Informed Neural Network (PINN) approach tailored for solving the Milne-Eddington (ME) inversion problem, specifically applied to spectropolarimetric observations from the Big Bear Solar Observatory's Near-InfraRed Imaging Spectropolarimeter (BBSO/NIRIS) at the Fe I 1.56 μm lines. Traditional ME inversion methods, though widely used, are computationally intensive, sensitive to noise, and often struggle to accurately capture complex profile asymmetries resulting from gradients in magnetic field strength, orientation, and line-of-sight velocities. By embedding the ME radiative transfer equations directly into the neural network training as physics-informed constraints, our MVPinn method robustly and efficiently retrieves magnetic field parameters, significantly outperforming traditional inversion methods in accuracy, noise resilience, and the ability to handle asymmetric and weak polarization signals. After training, MVPinn infers one magnetogram in about 15 seconds, compared to tens of minutes required by traditional ME inversion on high-resolution spectropolarimetric data. Quantitative comparisons demonstrate excellent agreement with well-established magnetic field measurements from the SDO/HMI and Hinode/SOT-SP instruments, with correlation coefficients of approximately 90%. In particular, MVPINN aligns better with Hinode/SOT-SP data, indicating some saturation of HMI data at high magnetic strengths. We further analyze the physical significance of profile asymmetries and the limitations inherent in the ME model assumption. Our results illustrate the potential of physics-informed machine learning methods in high-spatial-temporal solar observations, preparing for more sophisticated, real-time magnetic field analysis essential for current and next-generation solar telescopes and space weather monitoring.

astro-ph.SR↗

arXiv2025

Frequency-Resolved Forward Capacitance in GaN-based LEDs

This study establishes a unified framework for interpreting dynamic capacitive responses in InGaN-based light-emitting diodes (LEDs) through forward-bias capacitance-voltage-frequency spectroscopy. A hybrid impedance model integrating series RL components and parallel C-G networks was developed to resolve distinct frequency-dependent capacitive regimes. The low-frequency regime (<1 kHz) is governed by interfacial capacitance with characteristic reciprocal frequency dependence, while the mid-frequency range(10 kHz-6.4 MHz) demonstrates carrier diffusion and recombination dynamics. At MHz frequencies, negative capacitance manifests due to delayed carrier emission mediated by deep-level traps. The model achieved sub-1% fitting errors (R^2 > 0.99)across a broad bandwidth(10 kHz-6.4 MHz) , conclusively attributing negative capacitance to intrinsic trap processes rather than extrinsic artifacts. Critical advances include quantum well cap thickness modulation reducing mid-frequency capacitance by 30% and the dominance of trap-mediated inductance over parasitic contributions by three orders of magnitude. This framework resolves persistent controversies in LED impedance interpretation. By bridging semiconductor physics with device engineering, this methodology provides essential tools for designing next-generation optoelectronic systems requiring ultralow-latency operation and precise charge-state control.

physics.app-ph↗

arXiv2025

Beyond Single Concept Vector: Modeling Concept Subspace in LLMs with Gaussian Distribution

Probing learned concepts in large language models (LLMs) is crucial for understanding how semantic knowledge is encoded internally. Training linear classifiers on probing tasks is a principle approach to denote the vector of a certain concept in the representation space. However, the single vector identified for a concept varies with both data and training, making it less robust and weakening its effectiveness in real-world applications. To address this challenge, we propose an approach to approximate the subspace representing a specific concept. Built on linear probing classifiers, we extend the concept vectors into Gaussian Concept Subspace (GCS). We demonstrate GCS's effectiveness through measuring its faithfulness and plausibility across multiple LLMs with different sizes and architectures. Additionally, we use representation intervention tasks to showcase its efficacy in real-world applications such as emotion steering. Experimental results indicate that GCS concept vectors have the potential to balance steering performance and maintaining the fluency in natural language generation tasks.

cs.CL↗

arXiv2025

DeepOFormer: Deep Operator Learning with Domain-informed Features for Fatigue Life Prediction

Fatigue life characterizes the duration a material can function before failure under specific environmental conditions, and is traditionally assessed using stress-life (S-N) curves. While machine learning and deep learning offer promising results for fatigue life prediction, they face the overfitting challenge because of the small size of fatigue experimental data in specific materials. To address this challenge, we propose, DeepOFormer, by formulating S-N curve prediction as an operator learning problem. DeepOFormer improves the deep operator learning framework with a transformer-based encoder and a mean L2 relative error loss function. We also consider Stussi, Weibull, and Pascual and Meeker (PM) features as domain-informed features. These features are motivated by empirical fatigue models. To evaluate the performance of our DeepOFormer, we compare it with different deep learning models and XGBoost on a dataset with 54 S-N curves of aluminum alloys. With seven different aluminum alloys selected for testing, our DeepOFormer achieves an R2 of 0.9515, a mean absolute error of 0.2080, and a mean relative error of 0.5077, significantly outperforming state-of-the-art deep/machine learning methods including DeepONet, TabTransformer, and XGBoost, etc. The results highlight that our Deep0Former integrating with domain-informed features substantially improves prediction accuracy and generalization capabilities for fatigue life prediction in aluminum alloys.

cs.LG↗

arXiv2025

Red Emission from Strain-Relaxed Bulk InGaN Active Region

High-In-content InGaN quantum wells (QWs) in red light-emitting diodes (LEDs) are typically grown at low temperatures to ensure effective In incorporation. In this study, red LEDs based on bulk InGaN active region were demonstrated. The growth temperature of bulk InGaN was ~800C, which is over 100C higher than the typical growth temperature of red QWs. By introducing high-density trench structures in the underlying green multi-quantum wells (MQWs), the compressive strain in bulk InGaN was relaxed by ~96%. With strain relaxation, phase separation occurred in the bulk InGaN, forming low-In-content (blue) and high-In-content (red) phases. The red phase acted as carrier localization centers, enabling red light emission under electrical injection. The red LEDs based on bulk InGaN exhibited a peak wavelength of 645 nm at 20 mA, with on-wafer peak external quantum efficiency of 0.32%. This study presents a new epitaxial strategy for red InGaN LEDs.

cond-mat.mtrl-sci↗

arXiv2024

CodeV: Issue Resolving with Visual Data

Large Language Models (LLMs) have advanced rapidly in recent years, with their applications in software engineering expanding to more complex repository-level tasks. GitHub issue resolving is a key challenge among these tasks. While recent approaches have made progress on this task, they focus on textual data within issues, neglecting visual data. However, this visual data is crucial for resolving issues as it conveys additional knowledge that text alone cannot. We propose CodeV, the first approach to leveraging visual data to enhance the issue-resolving capabilities of LLMs. CodeV resolves each issue by following a two-phase process: data processing and patch generation. To evaluate CodeV, we construct a benchmark for visual issue resolving, namely Visual SWE-bench. Through extensive experiments, we demonstrate the effectiveness of CodeV, as well as provide valuable insights into leveraging visual data to resolve GitHub issues.

cs.SE↗

arXiv2024

IC Mechanisms for Risk-Averse Advertisers in the Online Advertising System

The autobidding system generates huge revenue for advertising platforms, garnering substantial research attention. Existing studies in autobidding systems focus on designing Autobidding Incentive Compatible (AIC) mechanisms, where the mechanism is Incentive Compatible (IC) under ex ante expectations. However, upon deploying AIC mechanisms in advertising platforms, we observe a notable deviation between the actual auction outcomes and these expectations during runtime, particularly in the scene with few clicks (sparse-click). This discrepancy undermines truthful bidding among advertisers in AIC mechanisms, especially for risk-averse advertisers who are averse to outcomes that do not align with the expectations. To address this issue, we propose a mechanism, Decoupled First-Price Auction (DFP), that retains its IC property even during runtime. DFP dynamically adjusts the payment based on real-time user conversion outcomes, ensuring that advertisers' realized utilities closely approximate their expected utilities during runtime. To realize the payment mechanism of DFP, we propose a PPO-based RL algorithm, with a meticulously crafted reward function. This algorithm dynamically adjusts the payment to fit DFP mechanism. We conduct extensive experiments leveraging real-world data to validate our findings.

cs.GT↗

arXiv2024

Unambiguous identification of the indirect band nature of atomically thin hexagonal boron nitride

Atomically thin hexagonal boron nitride (h-BN), especially monolayer, has garnered increasing attention due to its intriguing optical and light-matter-interaction properties. However, its intrinsic optical properties and electronic band structure, have long remained elusive. In this study, near-resonance excited deep-UV photoluminescence/Raman spectroscopy and deep-UV reflectance contrast spectroscopy are utilized to experimentally investigate the optical properties of atomically thin h-BN across various layer numbers. It is revealed that the absence of luminescence in 1-3 layers h-BN is indicative of their indirect band gap nature, rectifying previously adopted identification of a direct band gap in monolayer BN. Notably, band-edge luminescence signals and indirect bandgap absorption start to appear in 4-layer, and the luminescence intensity increases with the number of layers, suggesting that interlayer interactions and periodicity along the z-axis enhance phonon-assisted indirect bandgap transition, even in the 4-layer case, and furthermore indicating the formation process of flat bands at the K and M valleys as the periodicity along the z direction increases. Additionally, the prominent resonance Raman signals in atomically thin h-BN underscore strong electron-phonon coupling in this material.

cond-mat.mtrl-sci↗

arXiv2024

Single-atom-resolved vibrational spectroscopy of a dislocation

Phonon resistance from dislocation scattering is often divided into short-range core interactions and long-range strain field interactions. Using electron energy-loss spectroscopy on a GaN dislocation, we report observations of vibrational modes localized at specific core atoms (short-range) and strain-driven phonon energy shifts around the dislocation (long-range). Ab initio calculations support these findings and draw out additional details. This study reveals atomically resolved vibrational spectra of dislocations, thus offering insights for engineering improved material functionalities.

cond-mat.mtrl-sci↗

arXiv2024

GraphCoder: Enhancing Repository-Level Code Completion via Code Context Graph-based Retrieval and Language Model

The performance of repository-level code completion depends upon the effective leverage of both general and repository-specific knowledge. Despite the impressive capability of code LLMs in general code completion tasks, they often exhibit less satisfactory performance on repository-level completion due to the lack of repository-specific knowledge in these LLMs. To address this problem, we propose GraphCoder, a retrieval-augmented code completion framework that leverages LLMs' general code knowledge and the repository-specific knowledge via a graph-based retrieval-generation process. In particular, GraphCoder captures the context of completion target more accurately through code context graph (CCG) that consists of control-flow, data- and control-dependence between code statements, a more structured way to capture the completion target context than the sequence-based context used in existing retrieval-augmented approaches; based on CCG, GraphCoder further employs a coarse-to-fine retrieval process to locate context-similar code snippets with the completion target from the current repository. Experimental results demonstrate both the effectiveness and efficiency of GraphCoder: Compared to baseline retrieval-augmented methods, GraphCoder achieves higher exact match (EM) on average, with increases of +6.06 in code match and +6.23 in identifier match, while using less time and space.

cs.SE↗

arXiv2024

Global-local Fourier Neural Operator for Accelerating Coronal Magnetic Field Model

Exploring the outer atmosphere of the sun has remained a significant bottleneck in astrophysics, given the intricate magnetic formations that significantly influence diverse solar events. Magnetohydrodynamics (MHD) simulations allow us to model the complex interactions between the sun's plasma, magnetic fields, and the surrounding environment. However, MHD simulation is extremely time-consuming, taking days or weeks for simulation. The goal of this study is to accelerate coronal magnetic field simulation using deep learning, specifically, the Fourier Neural Operator (FNO). FNO has been proven to be an ideal tool for scientific computing and discovery in the literature. In this paper, we proposed a global-local Fourier Neural Operator (GL-FNO) that contains two branches of FNOs: the global FNO branch takes downsampled input to reconstruct global features while the local FNO branch takes original resolution input to capture fine details. The performance of the GLFNO is compared with state-of-the-art deep learning methods, including FNO, U-NO, U-FNO, Vision Transformer, CNN-RNN, and CNN-LSTM, to demonstrate its accuracy, computational efficiency, and scalability. Furthermore, physics analysis from domain experts is also performed to demonstrate the reliability of GL-FNO. The results demonstrate that GL-FNO not only accelerates the MHD simulation (a few seconds for prediction, more than \times 20,000 speed up) but also provides reliable prediction capabilities, thus greatly contributing to the understanding of space weather dynamics. Our code implementation is available at https://github.com/Yutao-0718/GL-FNO

astro-ph.SR↗

arXiv2024

Deep Computer Vision for Solar Physics Big Data: Opportunities and Challenges

With recent missions such as advanced space-based observatories like the Solar Dynamics Observatory (SDO) and Parker Solar Probe, and ground-based telescopes like the Daniel K. Inouye Solar Telescope (DKIST), the volume, velocity, and variety of data have made solar physics enter a transformative era as solar physics big data (SPBD). With the recent advancement of deep computer vision, there are new opportunities in SPBD for tackling problems that were previously unsolvable. However, there are new challenges arising due to the inherent characteristics of SPBD and deep computer vision models. This vision paper presents an overview of the different types of SPBD, explores new opportunities in applying deep computer vision to SPBD, highlights the unique challenges, and outlines several potential future research directions.

cs.CV↗

arXiv2024

SWE-bench-java: A GitHub Issue Resolving Benchmark for Java

GitHub issue resolving is a critical task in software engineering, recently gaining significant attention in both industry and academia. Within this task, SWE-bench has been released to evaluate issue resolving capabilities of large language models (LLMs), but has so far only focused on Python version. However, supporting more programming languages is also important, as there is a strong demand in industry. As a first step toward multilingual support, we have developed a Java version of SWE-bench, called SWE-bench-java. We have publicly released the dataset, along with the corresponding Docker-based evaluation environment and leaderboard, which will be continuously maintained and updated in the coming months. To verify the reliability of SWE-bench-java, we implement a classic method SWE-agent and test several powerful LLMs on it. As is well known, developing a high-quality multi-lingual benchmark is time-consuming and labor-intensive, so we welcome contributions through pull requests or collaboration to accelerate its iteration and refinement, paving the way for fully automated programming.

cs.SE↗