arXiv subjects

Ye Jiang

Publications and source records attributed to Ye Jiang.

At least 19 recordsLinked to original sources

arXiv2026

Horizon-redshift transfer in black-hole direct-wave damping

Direct waves from black-hole mergers may probe horizon dynamics, but their observed envelopes need not decay at the Kerr surface-gravity rate. We compute the complex-frequency spin-$-2$, $\ell=m=2$ Teukolsky response and combine it with a finite-duration near-horizon source whose outgoing amplitude is suppressed by gravitational redshift. The screened Kerr response to this finite-duration source produces an observable envelope damping $\geff<\kap$. For GW250114, this corresponds to $\geff\simeq0.4~{\rm ms}^{-1}$, consistent with a joint H1--L1 analysis of QNM-subtracted residuals. As a consistency check, the GW231226 remnant parameters give $\geff\simeq0.31~{\rm ms}^{-1}$, compatible with the event's residual profile. These results identify direct-wave envelope damping as an observable of horizon-redshift transfer rather than a direct measurement of surface gravity.

gr-qc↗

arXiv2026

A self-consistent EOB--Teukolsky framework for generic extreme mass-ratio inspirals

We present a full-relativistic waveform model for extreme mass-ratio inspirals (EMRIs) by self-consistently combining the effective one-body (EOB) formalism with the Teukolsky equation. The model incorporates analytical, mass-ratio-informed geodesic solutions within a deformed Kerr metric into the source term of the Teukolsky equation, establishing a direct connection between finite-mass-ratio orbital dynamics and gravitational-wave emission. The resulting frequency-domain formulation is coupled to a high-performance solver for the homogeneous Teukolsky equation, enabling rapid evaluation of the tens of thousands of modes required for accurate EMRI waveforms. We generate waveforms and radiation fluxes for generic Kerr orbits and investigate the influence of finite-mass-ratio corrections beyond the test-particle limit. The results show that mass-ratio-dependent deformations produce measurable modifications to radiation fluxes, and accumulated waveform phases over observationally relevant timescales. Our framework provides a generic-orbit EOB--Teukolsky waveform model for future space-based GW data analysis.

gr-qc↗

arXiv2025

Learn to Select: Exploring Label Distribution Divergence for In-Context Demonstration Selection in Text Classification

In-context learning (ICL) for text classification, which uses a few input-label demonstrations to describe a task, has demonstrated impressive performance on large language models (LLMs). However, the selection of in-context demonstrations plays a crucial role and can significantly affect LLMs' performance. Most existing demonstration selection methods primarily focus on semantic similarity between test inputs and demonstrations, often overlooking the importance of label distribution alignment. To address this limitation, we propose a two-stage demonstration selection method, TopK + Label Distribution Divergence (L2D), which leverages a fine-tuned BERT-like small language model (SLM) to generate label distributions and calculate their divergence for both test inputs and candidate demonstrations. This enables the selection of demonstrations that are not only semantically similar but also aligned in label distribution with the test input. Extensive experiments across seven text classification benchmarks show that our method consistently outperforms previous demonstration selection strategies. Further analysis reveals a positive correlation between the performance of LLMs and the accuracy of the underlying SLMs used for label distribution estimation.

cs.CL↗

arXiv2025

A New High-Performing Method for Solving the Homogeneous Teukolsky Equation

The numerical waveforms for the extreme mass-ratio inspirals (EMRIs) require a huge amount of homogeneous solutions of the Teukolsky equation in the frequency domain. The calculation accuracy and efficiency of the homogeneous solutions are the key performance bottleneck in waveform generation. In this paper, we propose a new numerical method based on the analytical series expansion which is most efficient for computing the homogeneous solutions with very high accuracy and a wider frequency range compared with the existing methods. Our new method is definitely useful for constructing the waveform templates of EMRIs.

gr-qc↗

arXiv2025

QUST_NLP at SemEval-2025 Task 7: A Three-Stage Retrieval Framework for Monolingual and Crosslingual Fact-Checked Claim Retrieval

This paper describes the participation of QUST_NLP in the SemEval-2025 Task 7. We propose a three-stage retrieval framework specifically designed for fact-checked claim retrieval. Initially, we evaluate the performance of several retrieval models and select the one that yields the best results for candidate retrieval. Next, we employ multiple re-ranking models to enhance the candidate results, with each model selecting the Top-10 outcomes. In the final stage, we utilize weighted voting to determine the final retrieval outcomes. Our approach achieved 5th place in the monolingual track and 7th place in the crosslingual track. We release our system code at: https://github.com/warmth27/SemEval2025_Task7

cs.IR↗

arXiv2025

Team QUST at SemEval-2025 Task 10: Evaluating Large Language Models in Multiclass Multi-label Classification of News Entity Framing

cs.CL↗

arXiv2025

Towards Realistic Detection Pipelines of Taiji: New Challenges in Data Analysis and High-Fidelity Simulations of Space-Based Gravitational Wave Antenna

Taiji, a Chinese space-based gravitational wave (GW) detection project, aims to explore the millihertz GW universe with unprecedented sensitivity. By observing astrophysical and cosmological sources, including Galactic binaries, massive black hole binaries, extreme mass-ratio inspirals, and stochastic gravitational wave backgrounds, etc., Taiji is expected to deliver transformative insights into astrophysics, cosmology, and fundamental physics. However, Taiji's data analysis faces unique challenges compared to ground-based detectors like LIGO-Virgo-KAGRA, such as the overlap of numerous signals, extended data durations, more rigorous accuracy requirements for the waveform templates, incompletely characterized noise spectra, non-stationary noises, and various data anomalies. Taking Taiji as a representative example, this paper reviews the data characteristics and data analysis challenges of space-based GW detection, and introduces the second round of Taiji Data Challenge, a collection of simulation datasets designed as a shared platform for resolving these critical issues. This platform distinguishes itself from previous works by the systematic integration of orbital dynamics based on a full drag-free and attitude control simulation, extended noise sources, more complicated and overlapping GW signals, second-generation time-delay interferometry, and the coupling effect of time-varying arm-lengths, etc. Concurrently released is the open-source toolkit Triangle, which offers the capabilities for customized simulation of signals, noises, and other instrumental effects. By taking a step further towards realistic detection, Taiji Data Challenge II and Triangle altogether serve as a new testbed, supporting the development of Taiji's global analysis and end-to-end pipelines, and ultimately bridging the gaps between observation and scientific objectives.

gr-qc↗

arXiv2024

The imprint of cosmic expansion history on the propagation of gravitational waves

Gravitational waves (GWs) are regarded as standard sirens for Cosmology. GWs from compact binary coalescence (CBC) can directly determine the luminosity distance but usually can not obtain information about the redshift. However, if the universe is not flat but accelerating, GWs should carry this cosmological effect. In this Letter, for the first time, we explore how the expansion of the Universe affects GW propagation by perturbing the Robertson-Walker metric. We achieve a comprehensive and rigorous formalism at the next-leading order to describe the cosmological acceleration in GWs from any kind of sources. Theoretically, this cosmological effect will obviously amplify GWs at frequencies as low as $10^{-12}$ Hz.

gr-qc↗

arXiv2024

AMPLE: Emotion-Aware Multimodal Fusion Prompt Learning for Fake News Detection

Detecting fake news in large datasets is challenging due to its diversity and complexity, with traditional approaches often focusing on textual features while underutilizing semantic and emotional elements. Current methods also rely heavily on large annotated datasets, limiting their effectiveness in more nuanced analysis. To address these challenges, this paper introduces Emotion-\textbf{A}ware \textbf{M}ultimodal Fusion \textbf{P}rompt \textbf{L}\textbf{E}arning (\textbf{AMPLE}) framework to address the above issue by combining text sentiment analysis with multimodal data and hybrid prompt templates. This framework extracts emotional elements from texts by leveraging sentiment analysis tools. It then employs Multi-Head Cross-Attention (MCA) mechanisms and similarity-aware fusion methods to integrate multimodal data. The proposed AMPLE framework demonstrates strong performance on two public datasets in both few-shot and data-rich settings, with results indicating the potential of emotional aspects in fake news detection. Furthermore, the study explores the impact of integrating large language models with this method for text sentiment extraction, revealing substantial room for further improvement. The code can be found at :\url{https://github.com/xxm1215/MMM2025_few-shot/

cs.CL↗

arXiv2024

Instruction Tuning Vs. In-Context Learning: Revisiting Large Language Models in Few-Shot Computational Social Science

Real-world applications of large language models (LLMs) in computational social science (CSS) tasks primarily depend on the effectiveness of instruction tuning (IT) or in-context learning (ICL). While IT has shown highly effective at fine-tuning LLMs for various tasks, ICL offers a rapid alternative for task adaptation by learning from examples without explicit gradient updates. In this paper, we evaluate the classification performance of LLMs using IT versus ICL in few-shot CSS tasks. The experimental results indicate that ICL consistently outperforms IT in most CSS tasks. Additionally, we investigate the relationship between the increasing number of training samples and LLM performance. Our findings show that simply increasing the number of samples without considering their quality does not consistently enhance the performance of LLMs with either ICL or IT and can sometimes even result in a performance decline. Finally, we compare three prompting strategies, demonstrating that ICL is more effective than zero-shot and Chain-of-Thought (CoT). Our research highlights the significant advantages of ICL in handling CSS tasks in few-shot settings and emphasizes the importance of optimizing sample quality and prompting strategies to improve LLM classification performance. The code will be made available.

cs.CL↗

arXiv2024

Large Visual-Language Models Are Also Good Classifiers: A Study of In-Context Multimodal Fake News Detection

Large visual-language models (LVLMs) exhibit exceptional performance in visual-language reasoning across diverse cross-modal benchmarks. Despite these advances, recent research indicates that Large Language Models (LLMs), like GPT-3.5-turbo, underachieve compared to well-trained smaller models, such as BERT, in Fake News Detection (FND), prompting inquiries into LVLMs' efficacy in FND tasks. Although performance could improve through fine-tuning LVLMs, the substantial parameters and requisite pre-trained weights render it a resource-heavy endeavor for FND applications. This paper initially assesses the FND capabilities of two notable LVLMs, CogVLM and GPT4V, in comparison to a smaller yet adeptly trained CLIP model in a zero-shot context. The findings demonstrate that LVLMs can attain performance competitive with that of the smaller model. Next, we integrate standard in-context learning (ICL) with LVLMs, noting improvements in FND performance, though limited in scope and consistency. To address this, we introduce the \textbf{I}n-context \textbf{M}ultimodal \textbf{F}ake \textbf{N}ews \textbf{D}etection (IMFND) framework, enriching in-context examples and test inputs with predictions and corresponding probabilities from a well-trained smaller model. This strategic integration directs the LVLMs' focus towards news segments associated with higher probabilities, thereby improving their analytical accuracy. The experimental results suggest that the IMFND framework significantly boosts the FND efficiency of LVLMs, achieving enhanced accuracy over the standard ICL approach across three publicly available FND datasets.

cs.CL↗

arXiv2024

Cross-Modal Augmentation for Few-Shot Multimodal Fake News Detection

The nascent topic of fake news requires automatic detection methods to quickly learn from limited annotated samples. Therefore, the capacity to rapidly acquire proficiency in a new task with limited guidance, also known as few-shot learning, is critical for detecting fake news in its early stages. Existing approaches either involve fine-tuning pre-trained language models which come with a large number of parameters, or training a complex neural network from scratch with large-scale annotated datasets. This paper presents a multimodal fake news detection model which augments multimodal features using unimodal features. For this purpose, we introduce Cross-Modal Augmentation (CMA), a simple approach for enhancing few-shot multimodal fake news detection by transforming n-shot classification into a more robust (n $\times$ z)-shot problem, where z represents the number of supplementary features. The proposed CMA achieves SOTA results over three benchmark datasets, utilizing a surprisingly simple linear probing method to classify multimodal fake news with only a few training samples. Furthermore, our method is significantly more lightweight than prior approaches, particularly in terms of the number of trainable parameters and epoch times. The code is available here: \url{https://github.com/zgjiangtoby/FND_fewshot}

cs.LG↗

arXiv2024

Improving the Automated Coronal Jet Identification with U-NET

Coronal jets are one of the most common eruptive activities in the solar atmosphere. They are related to rich physics processes, including but not limited to magnetic reconnection, flaring, instabilities, and plasma heating. Automated identification of off-limb coronal jets has been difficult due to their abundant nature, complex appearance, and relatively small size compared to other features in the corona. In this paper, we present an automated coronal jet identification algorithm (AJIA) that utilizes true and fake jets previously detected by a laborious semi-automated jet detection algorithm (SAJIA, Liu et al. 2023) as the input of an image segmentation neural network U-NET. It is found that AJIA could achieve a much higher (0.81) detecting precision than SAJIA (0.34), meanwhile giving the possibility of whether each pixel in an input image belongs to a jet. We demonstrate that with the aid of artificial neural networks, AJIA could enable fast, accurate, and real-time coronal jet identification from SDO/AIA 304 \AA observations, which are essential in studying the collective and long-term behavior of coronal jets and their relation with the solar activity cycles.

astro-ph.SR↗

arXiv2024

Monocular Localization with Semantics Map for Autonomous Vehicles

Accurate and robust localization remains a significant challenge for autonomous vehicles. The cost of sensors and limitations in local computational efficiency make it difficult to scale to large commercial applications. Traditional vision-based approaches focus on texture features that are susceptible to changes in lighting, season, perspective, and appearance. Additionally, the large storage size of maps with descriptors and complex optimization processes hinder system performance. To balance efficiency and accuracy, we propose a novel lightweight visual semantic localization algorithm that employs stable semantic features instead of low-level texture features. First, semantic maps are constructed offline by detecting semantic objects, such as ground markers, lane lines, and poles, using cameras or LiDAR sensors. Then, online visual localization is performed through data association of semantic features and map objects. We evaluated our proposed localization framework in the publicly available KAIST Urban dataset and in scenarios recorded by ourselves. The experimental results demonstrate that our method is a reliable and practical localization solution in various autonomous driving localization tasks.

cs.CV↗

arXiv2024

Team QUST at SemEval-2024 Task 8: A Comprehensive Study of Monolingual and Multilingual Approaches for Detecting AI-generated Text

This paper presents the participation of team QUST in Task 8 SemEval 2024. We first performed data augmentation and cleaning on the dataset to enhance model training efficiency and accuracy. In the monolingual task, we evaluated traditional deep-learning methods, multiscale positive-unlabeled framework (MPU), fine-tuning, adapters and ensemble methods. Then, we selected the top-performing models based on their accuracy from the monolingual models and evaluated them in subtasks A and B. The final model construction employed a stacking ensemble that combined fine-tuning with MPU. Our system achieved 8th (scored 8th in terms of accuracy, officially ranked 13th) place in the official test set in multilingual settings of subtask A. We release our system code at:https://github.com/warmth27/SemEval2024_QUST

cs.CL↗

arXiv2023

Resolving "dirty" effects around black holes by decoupling the Teukolsky equation

Detecting the environment around the supermassive black holes and tests of general relativity are important applications of extreme-mass-ratio inspirals (EMRIs). There is still a challenge to efficiently describe various "dirty" impacts on the inspirals like dark matter, gas, dipole radiation, electromagnetic interaction, and so on. In this Letter, we find the inherent linearity of the asymptotic solution of the inhomogeneous Teukolsky equation. Based on this property, we completely decouple the factors of the perturber and the background spacetime in the energy fluxes and waveforms. With the new decoupling form, the waveforms of EMRIs with non-geodesic motion in Kerr spacetime can be calculated conveniently. This will help to resolve the environment (including gas, field, dark matter, electromagnetic interaction, etc.) around the supermassive black holes and test general relativity.

gr-qc↗

arXiv2023

Prospect of detecting magnetic fields from strong-magnetized binary neutron stars

Binary neutron star mergers are unique sources of gravitational waves in multi-messenger astronomy. The inspiral phase of binary neutron stars can emit gravitational waves as chirp signals. The present waveform models of gravitational wave only considered the gravitational interaction. In this paper, we derive the waveform of the gravitational wave signal taking into account the presence of magnetic fields. We found that the electromagnetic interaction and radiation can introduce different frequency-dependent power laws for both amplitude and frequency of the gravitational wave. We show from the results of Fisher information matrix that the third-generation observation may detect magnetic dipole moments if the magnetic field is around 10^17 G.

gr-qc↗

arXiv2023

Influence of mass-ratio corrections in extreme-mass-ratio inspirals for testing general relativity

The popular waveform templates of extreme-mass-ratio inspirals usually omit the mass-ratio corrections in the conservative dynamics, and employ adiabatic approximation to include the radiation reaction. With the help of effective-one-body dynamics, we investigate the influence of mass-ratio corrections in the conservative part on the gravitational waves. We find that for the extra-relativistic orbits, the mass-ratio corrections can produce obvious orbital dephasing even for very small mass-ratio and then affect the waveforms. More importantly, it is noted that omitting the mass-ratio corrections in waveform templates may induce a fake signal of deviation from general relativity.

gr-qc↗