arXiv subjects

Fan Huang

Publications and source records attributed to Fan Huang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Detecting Cosmological Phase Transitions with Taiji: Sensitivity Analysis and Parameter Estimation

We investigate the capability of the Taiji space-based gravitational wave observatory to detect stochastic gravitational wave backgrounds produced by first-order phase transitions in the early universe. Using a comprehensive simulation framework that incorporates realistic instrumental noise, galactic double white dwarf confusion noise, and extragalactic compact binary backgrounds, we systematically analyze Taiji's sensitivity across a range of signal parameters. Our Bayesian analysis demonstrates that Taiji can robustly detect and characterize phase transition signals with energy densities exceeding $Ω_{\text{PT}} \gtrsim 1.4 \times 10^{-11}$ across most of its frequency band, with particularly strong sensitivity around $10^{-3}$ to $10^{-2}$ Hz. For signals with amplitudes above $Ω_{\text{PT}} \gtrsim 1.1 \times 10^{-10}$, Taiji can determine the peak frequency with relative precision better than $10\%$. These detection capabilities would enable Taiji to probe electroweak-scale phase transitions in various beyond-Standard-Model scenarios, potentially revealing new physics connected to baryogenesis and dark matter production. We quantify detection confidence using both Bayes factors and the Deviance Information Criterion, finding consistent results that validate our statistical methodology.

gr-qc↗

arXiv2025

The Wave Equation in the Context of Reduced Groups $C^*$-Algebras

Motivated by the identification $C(\mathbb{T})\cong C_r^*(\mathbb{Z})$ and the wave equation on the circle, we explore the wave equation in the context of reduced group $C^*$-algebras $C_r^*(G)$ for countably infinite, possibly non-abelian groups $G$. Using a one-parameter group of $*$-automorphisms whose infinitesimal generator paves the way to an analogue of the Laplacian, we establish the existence and uniqueness of solutions to the wave equation within this framework.

math.OA↗

arXiv2025

The Rise of Bluesky

This study investigates the rapid growth and evolving network structure of Bluesky from August 2023 to February 2025. Through multiple waves of user migrations, the platform has reached a stable, persistently active user base. The growth process has given rise to a dense follower network with clustering and hub features that favor viral information diffusion. These developments highlight engagement and structural similarities between Bluesky and established platforms.

cs.SI↗

arXiv2025

CMCRD: Cross-Modal Contrastive Representation Distillation for Emotion Recognition

Emotion recognition is an important component of affective computing, and also human-machine interaction. Unimodal emotion recognition is convenient, but the accuracy may not be high enough; on the contrary, multi-modal emotion recognition may be more accurate, but it also increases the complexity and cost of the data collection system. This paper considers cross-modal emotion recognition, i.e., using both electroencephalography (EEG) and eye movement in training, but only EEG or eye movement in test. We propose cross-modal contrastive representation distillation (CMCRD), which uses a pre-trained eye movement classification model to assist the training of an EEG classification model, improving feature extraction from EEG signals, or vice versa. During test, only EEG signals (or eye movement signals) are acquired, eliminating the need for multi-modal data. CMCRD not only improves the emotion recognition accuracy, but also makes the system more simplified and practical. Experiments using three different neural network architectures on three multi-modal emotion recognition datasets demonstrated the effectiveness of CMCRD. Compared with the EEG-only model, it improved the average classification accuracy by about 6.2%.

cs.HC↗

arXiv2025

Diffusion-augmented Graph Contrastive Learning for Collaborative Filter

Graph-based collaborative filtering has been established as a prominent approach in recommendation systems, leveraging the inherent graph topology of user-item interactions to model high-order connectivity patterns and enhance recommendation performance. Recent advances in Graph Contrastive Learning (GCL) have demonstrated promising potential to alleviate data sparsity issues by improving representation learning through contrastive view generation and mutual information maximization. However, existing approaches lack effective data augmentation strategies. Structural augmentation risks distorting fundamental graph topology, while feature-level perturbation techniques predominantly employ uniform noise scales that fail to account for node-specific characteristics. To solve these challenges, we propose Diffusion-augmented Contrastive Learning (DGCL), an innovative framework that integrates diffusion models with contrastive learning for enhanced collaborative filtering. Our approach employs a diffusion process that learns node-specific Gaussian distributions of representations, thereby generating semantically consistent yet diversified contrastive views through reverse diffusion sampling. DGCL facilitates adaptive data augmentation based on reconstructed representations, considering both semantic coherence and node-specific features. In addition, it explores unrepresented regions of the latent sparse feature space, thereby enriching the diversity of contrastive views. Extensive experimental results demonstrate the effectiveness of DGCL on three public datasets.

cs.IR↗

arXiv2024

MonoPCC: Photometric-invariant Cycle Constraint for Monocular Depth Estimation of Endoscopic Images

Photometric constraint is indispensable for self-supervised monocular depth estimation. It involves warping a source image onto a target view using estimated depth&pose, and then minimizing the difference between the warped and target images. However, the endoscopic built-in light causes significant brightness fluctuations, and thus makes the photometric constraint unreliable. Previous efforts only mitigate this relying on extra models to calibrate image brightness. In this paper, we propose MonoPCC to address the brightness inconsistency radically by reshaping the photometric constraint into a cycle form. Instead of only warping the source image, MonoPCC constructs a closed loop consisting of two opposite forward-backward warping paths: from target to source and then back to target. Thus, the target image finally receives an image cycle-warped from itself, which naturally makes the constraint invariant to brightness changes. Moreover, MonoPCC transplants the source image's phase-frequency into the intermediate warped image to avoid structure lost, and also stabilizes the training via an exponential moving average (EMA) strategy to avoid frequent changes in the forward warping. The comprehensive and extensive experimental results on four endoscopic datasets demonstrate that our proposed MonoPCC shows a great robustness to the brightness inconsistency, and exceeds other state-of-the-arts by reducing the absolute relative error by at least 7.27%, 9.38%, 9.90% and 3.17%, respectively.

cs.CV↗

arXiv2024

MonoBox: Tightness-free Box-supervised Polyp Segmentation using Monotonicity Constraint

We propose MonoBox, an innovative box-supervised segmentation method constrained by monotonicity to liberate its training from the user-unfriendly box-tightness assumption. In contrast to conventional box-supervised segmentation, where the box edges must precisely touch the target boundaries, MonoBox leverages imprecisely-annotated boxes to achieve robust pixel-wise segmentation. The 'linchpin' is that, within the noisy zones around box edges, MonoBox discards the traditional misguiding multiple-instance learning loss, and instead optimizes a carefully-designed objective, termed monotonicity constraint. Along directions transitioning from the foreground to background, this new constraint steers responses to adhere to a trend of monotonically decreasing values. Consequently, the originally unreliable learning within the noisy zones is transformed into a correct and effective monotonicity optimization. Moreover, an adaptive label correction is introduced, enabling MonoBox to enhance the tightness of box annotations using predicted masks from the previous epoch and dynamically shrink the noisy zones as training progresses. We verify MonoBox in the box-supervised segmentation task of polyps, where satisfying box-tightness is challenging due to the vague boundaries between the polyp and normal tissues. Experiments on both public synthetic and in-house real noisy datasets demonstrate that MonoBox exceeds other anti-noise state-of-the-arts by improving Dice by at least 5.5% and 3.3%, respectively. Codes are at https://github.com/Huster-Hq/MonoBox.

cs.CV↗

arXiv2024

ToBlend: Token-Level Blending With an Ensemble of LLMs to Attack AI-Generated Text Detection

The robustness of AI-content detection models against sophisticated adversarial strategies, such as paraphrasing or word switching, is a rising concern in natural language generation (NLG) applications. This study proposes ToBlend, a novel token-level ensemble text generation method to challenge the robustness of current AI-content detection approaches by utilizing multiple sets of candidate generative large language models (LLMs). By randomly sampling token(s) from candidate LLMs sets, we find ToBlend significantly drops the performance of most mainstream AI-content detection methods. We evaluate the text quality produced under different ToBlend settings based on annotations from experienced human experts. We proposed a fine-tuned Llama3.1 model to distinguish the ToBlend generated text more accurately. Our findings underscore our proposed text generation approach's great potential in deceiving and improving detection models. Our datasets, codes, and annotations are open-sourced.

cs.CL↗

arXiv2024

AI.vs.Clinician: Unveiling Intricate Interactions Between AI and Clinicians through an Open-Access Database

Artificial Intelligence (AI) plays a crucial role in medical field and has the potential to revolutionize healthcare practices. However, the success of AI models and their impacts hinge on the synergy between AI and medical specialists, with clinicians assuming a dominant role. Unfortunately, the intricate dynamics and interactions between AI and clinicians remain undiscovered and thus hinder AI from being translated into medical practice. To address this gap, we have curated a groundbreaking database called AI.vs.Clinician. This database is the first of its kind for studying the interactions between AI and clinicians. It derives from 7,500 collaborative diagnosis records on a life-threatening medical emergency -- Sepsis -- from 14 medical centers across China. For the patient cohorts well-chosen from MIMIC databases, the AI-related information comprises the model property, feature input, diagnosis decision, and inferred probabilities of sepsis onset presently and within next three hours. The clinician-related information includes the viewed examination data and sequence, viewed time, preliminary and final diagnosis decisions with or without AI assistance, and recommended treatment.

cs.HC↗

arXiv2024

Establishing Rigorous and Cost-effective Clinical Trials for Artificial Intelligence Models

A profound gap persists between artificial intelligence (AI) and clinical practice in medicine, primarily due to the lack of rigorous and cost-effective evaluation methodologies. State-of-the-art and state-of-the-practice AI model evaluations are limited to laboratory studies on medical datasets or direct clinical trials with no or solely patient-centered controls. Moreover, the crucial role of clinicians in collaborating with AI, pivotal for determining its impact on clinical practice, is often overlooked. For the first time, we emphasize the critical necessity for rigorous and cost-effective evaluation methodologies for AI models in clinical practice, featuring patient/clinician-centered (dual-centered) AI randomized controlled trials (DC-AI RCTs) and virtual clinician-based in-silico trials (VC-MedAI) as an effective proxy for DC-AI RCTs. Leveraging 7500 diagnosis records from two-step inaugural DC-AI RCTs across 14 medical centers with 125 clinicians, our results demonstrate the necessity of DC-AI RCTs and the effectiveness of VC-MedAI. Notably, VC-MedAI performs comparably to human clinicians, replicating insights and conclusions from prospective DC-AI RCTs. We envision DC-AI RCTs and VC-MedAI as pivotal advancements, presenting innovative and transformative evaluation methodologies for AI models in clinical practice, offering a preclinical-like setting mirroring conventional medicine, and reshaping development paradigms in a cost-effective and fast-iterative manner. Chinese Clinical Trial Registration: ChiCTR2400086816.

cs.AI↗

arXiv2024

Multiple Bound States in the Continuum: Towards Intense Terahertz Matter Interaction

Bound states in the continuum (BICs) are an excellent platform enabling highly efficient light-matter interaction in applications for lasing, nonlinear generation, and sensing. However, the current focus in implementing BICs has primarily been on single sharp resonances, limiting the extent of electric field enhancement for multiple resonances. In this study, we conducted experimental demonstrations to showcase how metasurfaces can enable the control of symmetry-broken and Friedrich-Wintgen BICs by leveraging the asymmetry of split resonant rings. This approach allows for the existence of multiple free-control BIC resonances and tailored enhancement of controlling light-matter interactions. We have conducted further experiments to validate the effectiveness and performance of our approach for identification of the distinct fingerprint of α-lactose with high sensitivity using only one single metasurface. These findings present a novel and efficient platform for the development of miniaturized and chip-scale photonics devices with intense light-matter interaction.

physics.optics↗

arXiv2024

ChatGPT Rates Natural Language Explanation Quality Like Humans: But on Which Scales?

As AI becomes more integral in our lives, the need for transparency and responsibility grows. While natural language explanations (NLEs) are vital for clarifying the reasoning behind AI decisions, evaluating them through human judgments is complex and resource-intensive due to subjectivity and the need for fine-grained ratings. This study explores the alignment between ChatGPT and human assessments across multiple scales (i.e., binary, ternary, and 7-Likert scale). We sample 300 data instances from three NLE datasets and collect 900 human annotations for both informativeness and clarity scores as the text quality measurement. We further conduct paired comparison experiments under different ranges of subjectivity scores, where the baseline comes from 8,346 human annotations. Our results show that ChatGPT aligns better with humans in more coarse-grained scales. Also, paired comparisons and dynamic prompting (i.e., providing semantically similar examples in the prompt) improve the alignment. This research advances our understanding of large language models' capabilities to assess the text explanation quality in different configurations for responsible AI development.

cs.CL↗

arXiv2023

Cold & Warm Net: Addressing Cold-Start Users in Recommender Systems

Cold-start recommendation is one of the major challenges faced by recommender systems (RS). Herein, we focus on the user cold-start problem. Recently, methods utilizing side information or meta-learning have been used to model cold-start users. However, it is difficult to deploy these methods to industrial RS. There has not been much research that pays attention to the user cold-start problem in the matching stage. In this paper, we propose Cold & Warm Net based on expert models who are responsible for modeling cold-start and warm-up users respectively. A gate network is applied to incorporate the results from two experts. Furthermore, dynamic knowledge distillation acting as a teacher selector is introduced to assist experts in better learning user representation. With comprehensive mutual information, features highly relevant to user behavior are selected for the bias net which explicitly models user behavior bias. Finally, we evaluate our Cold & Warm Net on public datasets in comparison to models commonly applied in the matching stage and it outperforms other models on all user types. The proposed model has also been deployed on an industrial short video platform and achieves a significant increase in app dwell time and user retention rate.

cs.IR↗

arXiv2023

Impacts of Gravitational-Wave Background from Supermassive Black Hole Binaries on the Detection of Compact Binaries by LISA

In the frequency band of Laser Interferometer Space Antenna (LISA), extensive research has been conducted on the impact of foreground confusion noise generated by galactic binaries within the Milky Way galaxy. Additionally, the recent evidence for a stochastic signal, announced by the NANOGrav, EPTA, PPTA, CPTA and InPTA, indicates that the stochastic gravitational-wave background generated by supermassive black hole binaries (SMBHBs) can contribute a strong background noise within in LISA band. Given the presence of such strong noise, it is expected to have a considerable impacts on LISA's scientific missions. In this work, we investigate the impacts of the SGWB generated by SMBHBs on the detection of massive black hole binaries (MBHBs), verified galactic binaries (VGBs) and extreme mass ratio inspirals (EMRIs) in the context of LISA, and find it crucial to resolve and eliminate the exceed noise from the SGWB to ensure the success of LISA's missions.

astro-ph.CO↗

arXiv2023

YouNICon: YouTube's CommuNIty of Conspiracy Videos

Conspiracy theories are widely propagated on social media. Among various social media services, YouTube is one of the most influential sources of news and entertainment. This paper seeks to develop a dataset, YOUNICON, to enable researchers to perform conspiracy theory detection as well as classification of videos with conspiracy theories into different topics. YOUNICON is a dataset with a large collection of videos from suspicious channels that were identified to contain conspiracy theories in a previous study (Ledwich and Zaitsev 2020). Overall, YOUNICON will enable researchers to study trends in conspiracy theories and understand how individuals can interact with the conspiracy theory producing community or channel. Our data is available at: https://doi.org/10.5281/zenodo.7466262.

cs.CY↗

arXiv2023

Chain of Explanation: New Prompting Method to Generate Higher Quality Natural Language Explanation for Implicit Hate Speech

Recent studies have exploited advanced generative language models to generate Natural Language Explanations (NLE) for why a certain text could be hateful. We propose the Chain of Explanation (CoE) Prompting method, using the heuristic words and target group, to generate high-quality NLE for implicit hate speech. We improved the BLUE score from 44.0 to 62.3 for NLE generation by providing accurate target information. We then evaluate the quality of generated NLE using various automatic metrics and human annotations of informativeness and clarity scores.

cs.CL↗

arXiv2023

Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech

Recent studies have alarmed that many online hate speeches are implicit. With its subtle nature, the explainability of the detection of such hateful speech has been a challenging problem. In this work, we examine whether ChatGPT can be used for providing natural language explanations (NLEs) for implicit hateful speech detection. We design our prompt to elicit concise ChatGPT-generated NLEs and conduct user studies to evaluate their qualities by comparison with human-written NLEs. We discuss the potential and limitations of ChatGPT in the context of implicit hateful speech research.

cs.CL↗

arXiv2023

Random Padding Data Augmentation

The convolutional neural network (CNN) learns the same object in different positions in images, which can improve the recognition accuracy of the model. An implication of this is that CNN may know where the object is. The usefulness of the features' spatial information in CNNs has not been well investigated. In this paper, we found that the model's learning of features' position information hindered the learning of the features' relationship. Therefore, we introduced Random Padding, a new type of padding method for training CNNs that impairs the architecture's capacity to learn position information by adding zero-padding randomly to half of the border of feature maps. Random Padding is parameter-free, simple to construct, and compatible with the majority of CNN-based recognition models. This technique is also complementary to data augmentations such as random cropping, rotation, flipping and erasing, and consistently improves the performance of image classification over strong baselines.

cs.CV↗