arXiv subjects

Yun Wang

Publications and source records attributed to Yun Wang.

At least 109 records · Page 6Linked to original sources

arXiv2025

A period-increasing oscillation signal in a long gamma-ray burst

Gamma-ray bursts (GRBs), the brightest electromagnetic bursts in the universe, are believed to originate from ultra-relativistic jets launched by the rapidly rotating central engine, either a disk-surrounded newly formed black hole (BH) or a magnetar. Such a central engine potentially possesses rapidly evolving physical characteristics, as it is just born. The caught of time-increasing frequency in the gravitational wave signals is the evidence of upon opinion. Here we report a possible oscillatory signal identified in GRB 131122B with periods increasing from 1.27 seconds to 4.02 seconds in a time interval of 16.75 seconds. Such a peculiar oscillation signal has not been identified in GRBs before and its periodic evolution could also be the quickest one found in the electromagnetic radiation window of astrophysics. The precession of a misaligned accretion disk caused by the tidal disruption of a star by an intermediate-mass BH may be responsible for this signal. This finding could open a new window to reveal the nature of the hiding central engine of GRBs.

astro-ph.HE↗

arXiv2025

A system capable of verifiably and privately screening global DNA synthesis

Printing custom DNA sequences is essential to scientific and biomedical research, but the technology can be used to manufacture plagues as well as cures. Just as ink printers recognize and reject attempts to counterfeit money, DNA synthesizers and assemblers should deny unauthorized requests to make viral DNA that could be misused. There are three complications. First, we don't need to quickly update printers to deal with newly discovered currencies, whereas we regularly learn of new potential pandemic viruses and other biological threats. Second, convincing counterfeit bills can't be printed in small pieces and taped together, while preventing the distributed synthesis and subsequent re-assembly of controlled sequences will require tracking which DNA fragments have been ordered across all providers and benchtop devices while protecting legitimate customer privacy. Finally, counterfeiting can at worst undermine faith in currency, whereas unauthorized DNA synthesis could be used to deliberately cause pandemics. Here we describe SecureDNA, a free, privacy-preserving, and fully automated system capable of verifiably screening all DNA synthesis orders of 30+ nucleotides against an up-to-date database of controlled sequences, and its operational performance and specificity when applied to 67 million nucleotides of DNA synthesized by providers in the United States, Europe, and China.

cs.CR↗

arXiv2025

Leveraging large language models and traditional machine learning ensembles for ADHD detection from narrative transcripts

Despite rapid advances in large language models (LLMs), their integration with traditional supervised machine learning (ML) techniques that have proven applicability to medical data remains underexplored. This is particularly true for psychiatric applications, where narrative data often exhibit nuanced linguistic and contextual complexity, and can benefit from the combination of multiple models with differing characteristics. In this study, we introduce an ensemble framework for automatically classifying Attention-Deficit/Hyperactivity Disorder (ADHD) diagnosis (binary) using narrative transcripts. Our approach integrates three complementary models: LLaMA3, an open-source LLM that captures long-range semantic structure; RoBERTa, a pre-trained transformer model fine-tuned on labeled clinical narratives; and a Support Vector Machine (SVM) classifier trained using TF-IDF-based lexical features. These models are aggregated through a majority voting mechanism to enhance predictive robustness. The dataset includes 441 instances, including 352 for training and 89 for validation. Empirical results show that the ensemble outperforms individual models, achieving an F$_1$ score of 0.71 (95\% CI: [0.60-0.80]). Compared to the best-performing individual model (SVM), the ensemble improved recall while maintaining competitive precision. This indicates the strong sensitivity of the ensemble in identifying ADHD-related linguistic cues. These findings demonstrate the promise of hybrid architectures that leverage the semantic richness of LLMs alongside the interpretability and pattern recognition capabilities of traditional supervised ML, offering a new direction for robust and generalizable psychiatric text classification.

cs.CL↗

arXiv2025

Artificial Intelligence Bias on English Language Learners in Automatic Scoring

This study investigated potential scoring biases and disparities toward English Language Learners (ELLs) when using automatic scoring systems for middle school students' written responses to science assessments. We specifically focus on examining how unbalanced training data with ELLs contributes to scoring bias and disparities. We fine-tuned BERT with four datasets: responses from (1) ELLs, (2) non-ELLs, (3) a mixed dataset reflecting the real-world proportion of ELLs and non-ELLs (unbalanced), and (4) a balanced mixed dataset with equal representation of both groups. The study analyzed 21 assessment items: 10 items with about 30,000 ELL responses, five items with about 1,000 ELL responses, and six items with about 200 ELL responses. Scoring accuracy (Acc) was calculated and compared to identify bias using Friedman tests. We measured the Mean Score Gaps (MSGs) between ELLs and non-ELLs and then calculated the differences in MSGs generated through both the human and AI models to identify the scoring disparities. We found that no AI bias and distorted disparities between ELLs and non-ELLs were found when the training dataset was large enough (ELL = 30,000 and ELL = 1,000), but concerns could exist if the sample size is limited (ELL = 200).

cs.CL↗

arXiv2025

SPAST: Arbitrary Style Transfer with Style Priors via Pre-trained Large-scale Model

Given an arbitrary content and style image, arbitrary style transfer aims to render a new stylized image which preserves the content image's structure and possesses the style image's style. Existing arbitrary style transfer methods are based on either small models or pre-trained large-scale models. The small model-based methods fail to generate high-quality stylized images, bringing artifacts and disharmonious patterns. The pre-trained large-scale model-based methods can generate high-quality stylized images but struggle to preserve the content structure and cost long inference time. To this end, we propose a new framework, called SPAST, to generate high-quality stylized images with less inference time. Specifically, we design a novel Local-global Window Size Stylization Module (LGWSSM)tofuse style features into content features. Besides, we introduce a novel style prior loss, which can dig out the style priors from a pre-trained large-scale model into the SPAST and motivate the SPAST to generate high-quality stylized images with short inference time.We conduct abundant experiments to verify that our proposed method can generate high-quality stylized images and less inference time compared with the SOTA arbitrary style transfer methods.

cs.CV↗

arXiv2025

Enhanced Photovoltaic Power Forecasting: An iTransformer and LSTM-Based Model Integrating Temporal and Covariate Interactions

Accurate photovoltaic (PV) power forecasting is critical for integrating renewable energy sources into the grid, optimizing real-time energy management, and ensuring energy reliability amidst increasing demand. However, existing models often struggle with effectively capturing the complex relationships between target variables and covariates, as well as the interactions between temporal dynamics and multivariate data, leading to suboptimal forecasting accuracy. To address these challenges, we propose a novel model architecture that leverages the iTransformer for feature extraction from target variables and employs long short-term memory (LSTM) to extract features from covariates. A cross-attention mechanism is integrated to fuse the outputs of both models, followed by a Kolmogorov-Arnold network (KAN) mapping for enhanced representation. The effectiveness of the proposed model is validated using publicly available datasets from Australia, with experiments conducted across four seasons. Results demonstrate that the proposed model effectively capture seasonal variations in PV power generation and improve forecasting accuracy.

cs.LG↗

arXiv2025

Point Singularities of Solutions to the Stationary Incompressible MHD Equations

We investigate the point singularity of very weak solutions $(\mathbf{u},\mathbf{B})$ to the stationary MHD equations. More precisely, assume that the solution $(\mathbf{u},\mathbf{B})$ in the punctured ball $B_2\setminus \{0\}$ satisfies the vanishing condition (4), and that $|\mathbf{u}(x)|\le \varepsilon |x|^{-1},\ |\mathbf{B}(x)|\le C |x|^{-1}$ with small $\varepsilon>0$ and general $C>0$. Then, the leading order term of $\mathbf{u}$ is a Landau solution, while the $(-1)$ order term of $\mathbf{B}$ is $0$. In particular, for axisymmetric solutions $(\mathbf{u}, \mathbf{B})$, the condition (4) holds provided $\mathbf{B} = B^θ(r,z) \mathbf{e}_θ$ or the boundary condition (7) is imposed.

math.AP↗

arXiv2025

Saliency-driven Dynamic Token Pruning for Large Language Models

Despite the recent success of large language models (LLMs), LLMs are particularly challenging in long-sequence inference scenarios due to the quadratic computational complexity of the attention mechanism. Inspired by the interpretability theory of feature attribution in neural network models, we observe that not all tokens have the same contribution. Based on this observation, we propose a novel token pruning framework, namely Saliency-driven Dynamic Token Pruning (SDTP), to gradually and dynamically prune redundant tokens based on the input context. Specifically, a lightweight saliency-driven prediction module is designed to estimate the importance score of each token with its hidden state, which is added to different layers of the LLM to hierarchically prune redundant tokens. Furthermore, a ranking-based optimization strategy is proposed to minimize the ranking divergence of the saliency score and the predicted importance score. Extensive experiments have shown that our framework is generalizable to various models and datasets. By hierarchically pruning 65\% of the input tokens, our method greatly reduces 33\% $\sim$ 47\% FLOPs and achieves speedup up to 1.75$\times$ during inference, while maintaining comparable performance. We further demonstrate that SDTP can be combined with KV cache compression method for further compression.

cs.CL↗

arXiv2025

Probable evidence for a transient mega-electron volt emission line in the GRB 221023A

Detection of spectral line in gamma-ray bursts (GRBs) is importance for studying GRB physics, as it provides insights into the composition and physical conditions of the GRB environment. However, progress in detecting X-ray or gamma-ray emission and absorption lines in GRB spectra has been relatively slow, only the narrow emission line feature of about 10 MeV found in GRB 221009A has exhibited a significance exceeding $5 σ$. Here, we report the probable evidence of a narrow emission feature at about 2.1 mega-electron volts (MeV) in the spectrum of GRB 221023A. The highest statistical significance of this feature is observed in the time interval between 8 and 30 seconds after Fermi Gamma-Ray Burst Monitor trigger, with the chance probability value $<2.56 \times 10^{-5}$ (after accounting for the look-elsewhere effect), corresponding to a Gaussian-equivalent significance $> 4.20 σ$. We interpret this feature as being generated through the de-excitation of excited electrons in the relativistic hydrogen-like high-atomic-number ions entrained in the GRB jet.

astro-ph.HE↗

arXiv2025

Calculating the EFT likelihood via saddle-point expansion

In this paper, we extend the functional approach for calculating the EFT likelihood by applying the saddle-point expansion. We demonstrate that, after suitable reformulation, the likelihood expression is consistent with the path integral required to be computed in the theory of false vacuum decay. In contrast to the saddle-point approximation, the application of the saddle-point expansion necessitates more nuanced considerations, particularly concerning the treatment of the negative eigenvalues of the second derivative of the action at the saddle point. We illustrate that a similar issue arises in the likelihood calculation, which requires approximating the original integral contour through the combination of the steepest descent contours in the field space. As a concrete example, we focus on calculating the EFT likelihood under a Gaussian distribution and propose a general procedure for computing the likelihood using the saddle-point expansion method for arbitrary partition functions. Precise computation of the likelihood will benefit Bayesian forward modeling, thereby enabling more reliable theoretical predictions.

hep-ph↗

arXiv2025

Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling

Data storytelling plays an important role in data workers' daily jobs since it boosts team collaboration and public communication. However, to make an appealing data story, data workers spend tremendous efforts on various tasks, including outlining and styling the story. Recently, a growing research trend has been exploring how to assist data storytelling with advanced artificial intelligence (AI). However, existing studies may focus on individual tasks in the workflow of data storytelling and do not reveal a complete picture of humans' preference for collaborating with AI. To better understand real-world needs, we interviewed eighteen data workers from both industry and academia to learn where and how they would like to collaborate with AI. Surprisingly, though the participants showed excitement about collaborating with AI, many of them also expressed reluctance and pointed out nuanced reasons. Based on their responses, we first characterize stages and tasks in the practical data storytelling workflows and the desired roles of AI. Then the preferred collaboration patterns in different tasks are identified. Next, we summarize the interviewees' reasons why and why not they would like to collaborate with AI. Finally, we provide suggestions for human-AI collaborative data storytelling to hopefully shed light on future related research.

cs.HC↗

arXiv2025

CWTHF: Identifying Dark Matter Halos with Continuous Wavelet Transform

Cosmological simulations are an important method for investigating the evolution of the Universe. In order to gain further insight into the processes of structure formation, it is necessary to identify isolated bound objects within the simulations, namely, the dark matter halos. The continuous wavelet transform (CWT) is an effective tool used as a halo finder due to its ability to extract clustering information from the input data. In this study, we introduce CWTHF (Continuous Wavelet Transform Halo Finder), the first wavelet-based, MPI-parallelized halo finder, marking a novel approach in the field of cosmology. We calculate the CWT from the cloud-in-cell (CIC) grid and segment the grid based on the local CWT maxima. We then investigate the effects of the parameters that influence our program and identify the default settings. A comparison with the conventional friends-of-friends (FOF) method demonstrates the viability of CWT for halo finding. Although the actual performance is not faster than FOF, the linear time complexity of $\mathcal{O}(N)$ of our identification scheme indicates its significant potential for future optimization and application.

astro-ph.CO↗

arXiv2025

A High-Accuracy Alignment Approach for Solar Images of Different Wavelengths

Image alignment plays a crucial role in solar physics research, primarily involving translation, rotation, and scaling. \G{The different wavelength images of the chromosphere and transition region have structural complexity and differences in similarity, which poses a challenge to their alignment.} Therefore, a novel alignment approach based on dense optical flow (OF) and the RANSAC algorithm is proposed in this paper. \G{It takes the OF vectors of similar regions between images to be used as feature points for matching. Then, it calculates scaling, rotation, and translation.} The study selects three wavelengths for two groups of alignment experiments: the 304 Å of the Atmospheric Imaging Assembly (AIA), the 1216 Å of the Solar Disk Imager (SDI), and the 465 Å of the Solar Upper Transition Region Imager (SUTRI). Two methods are used to evaluate alignment accuracy: Monte Carlo simulation and Uncertainty Analysis Based on the Jacobian Matrix (UABJM). \G{The evaluation results indicate that this approach achieves sub-pixel accuracy in the alignment of AIA 304 Å and SDI 1216 Å, while demonstrating higher accuracy in the alignment of AIA 304 Å and SUTRI 465 Å, which have greater similarity.

astro-ph.IM↗

arXiv2025

DyArtbank: Diverse Artistic Style Transfer via Pre-trained Stable Diffusion and Dynamic Style Prompt Artbank

Artistic style transfer aims to transfer the learned style onto an arbitrary content image. However, most existing style transfer methods can only render consistent artistic stylized images, making it difficult for users to get enough stylized images to enjoy. To solve this issue, we propose a novel artistic style transfer framework called DyArtbank, which can generate diverse and highly realistic artistic stylized images. Specifically, we introduce a Dynamic Style Prompt ArtBank (DSPA), a set of learnable parameters. It can learn and store the style information from the collection of artworks, dynamically guiding pre-trained stable diffusion to generate diverse and highly realistic artistic stylized images. DSPA can also generate random artistic image samples with the learned style information, providing a new idea for data augmentation. Besides, a Key Content Feature Prompt (KCFP) module is proposed to provide sufficient content prompts for pre-trained stable diffusion to preserve the detailed structure of the input content image. Extensive qualitative and quantitative experiments verify the effectiveness of our proposed method. Code is available: https://github.com/Jamie-Cheung/DyArtbank

cs.CV↗

arXiv2025

A Sampling-based Framework for Hypothesis Testing on Large Attributed Graphs

Hypothesis testing is a statistical method used to draw conclusions about populations from sample data, typically represented in tables. With the prevalence of graph representations in real-life applications, hypothesis testing in graphs is gaining importance. In this work, we formalize node, edge, and path hypotheses in attributed graphs. We develop a sampling-based hypothesis testing framework, which can accommodate existing hypothesis-agnostic graph sampling methods. To achieve accurate and efficient sampling, we then propose a Path-Hypothesis-Aware SamplEr, PHASE, an m- dimensional random walk that accounts for the paths specified in a hypothesis. We further optimize its time efficiency and propose PHASEopt. Experiments on real datasets demonstrate the ability of our framework to leverage common graph sampling methods for hypothesis testing, and the superiority of hypothesis-aware sampling in terms of accuracy and time efficiency.

stat.ML↗

arXiv2025

Exploring Embodied Emotional Communication: A Human-oriented Review of Mediated Social Touch

This paper offers a structured understanding of mediated social touch (MST) using a human-oriented approach, through an extensive review of literature spanning tactile interfaces, emotional information, mapping mechanisms, and the dynamics of human-human and human-robot interactions. By investigating the existing and exploratory mapping strategies of the 37 selected MST cases, we established the emotional expression space of MSTs that accommodated a diverse spectrum of emotions by integrating the categorical and Valence-arousal models, showcasing how emotional cues can be translated into tactile signals. Based on the expressive capacity of MSTs, a practical design space was structured encompassing factors such as the body locations, device form, tactile modalities, and parameters. We also proposed various design strategies for MSTs including workflow, evaluation methods, and ethical and cultural considerations, as well as several future research directions. MSTs' potential is reflected not only in conveying emotional information but also in fostering empathy, comfort, and connection in both human-human and human-robot interactions. This paper aims to serve as a comprehensive reference for design researchers and practitioners, which helps expand the scope of emotional communication of MSTs, facilitating the exploration of diverse applications of affective haptics, and enhancing the naturalness and sociability of haptic interaction.

cs.HC↗

arXiv2025

Self-Similar Solutions to the steady Navier-Stokes Equations in a two-dimensional sector

This paper is concerned with self-similar solutions of the steady Navier-Stokes system in a two-dimensional sector with the no-slip boundary condition. We give necessary and sufficient conditions in terms of the angle of the sector and the flux to guarantee the existence of self-similar solutions of a given type. We also investigate the uniqueness and non-uniqueness of flows with a given type, which not only give rigorous justifications for some statements in \cite{Rosenhead40} but also show that some numerical computations in \cite{Rosenhead40} may not be precise. The non-uniqueness result is a new phenomenon for these flows. As a consequence of the classification of self-similar solutions in the half-space, we characterize the leading order term of the steady Navier-Stokes system in an aperture domain when the flux is small. The main approach is to study the ODE system governing self-similar solutions, where the detailed properties of both complete and incomplete elliptic functions have been investigated.

math.AP↗

arXiv2025

Reflecting on Design Paradigms of Animated Data Video Tools

Animated data videos have gained significant popularity in recent years. However, authoring data videos remains challenging due to the complexity of creating and coordinating diverse components (e.g., visualization, animation, audio, etc.). Although numerous tools have been developed to streamline the process, there is a lack of comprehensive understanding and reflection of their design paradigms to inform future development. To address this gap, we propose a framework for understanding data video creation tools along two dimensions: what data video components to create and coordinate, including visual, motion, narrative, and audio components, and how to support the creation and coordination. By applying the framework to analyze 46 existing tools, we summarized key design paradigms of creating and coordinating each component based on the varying work distribution for humans and AI in these tools. Finally, we share our detailed reflections, highlight gaps from a holistic view, and discuss future directions to address them.

cs.HC↗