arXiv subjects

Xin Wang

Publications and source records attributed to Xin Wang.

At least 757 records · Page 42Linked to original sources

arXiv2025

AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark

AI-generated faces have enriched human life, such as entertainment, education, and art. However, they also pose misuse risks. Therefore, detecting AI-generated faces becomes crucial, yet current detectors show biased performance across different demographic groups. Mitigating biases can be done by designing algorithmic fairness methods, which usually require demographically annotated face datasets for model training. However, no existing dataset encompasses both demographic attributes and diverse generative methods simultaneously, which hinders the development of fair detectors for AI-generated faces. In this work, we introduce the AI-Face dataset, the first million-scale demographically annotated AI-generated face image dataset, including real faces, faces from deepfake videos, and faces generated by Generative Adversarial Networks and Diffusion Models. Based on this dataset, we conduct the first comprehensive fairness benchmark to assess various AI face detectors and provide valuable insights and findings to promote the future fair design of AI face detectors. Our AI-Face dataset and benchmark code are publicly available at https://github.com/Purdue-M2/AI-Face-FairnessBench

cs.CV↗

arXiv2025

Limitations of Amplitude Encoding on Quantum Classification

It remains unclear whether quantum machine learning (QML) has real advantages when dealing with practical and meaningful tasks. Encoding classical data into quantum states is one of the key steps in QML. Amplitude encoding has been widely used owing to its remarkable efficiency in encoding a number of $2^{n}$ classical data into $n$ qubits simultaneously. However, the theoretical impact of amplitude encoding on QML has not been thoroughly investigated. In this work we prove that under some broad and typical data assumptions, the average of encoded quantum states via amplitude encoding tends to concentrate towards a specific state. This concentration phenomenon severely constrains the capability of quantum classifiers as it leads to a loss barrier phenomenon, namely, the loss function has a lower bound that cannot be improved by any optimization algorithm. In addition, via numerical simulations, we reveal a counterintuitive phenomenon of amplitude encoding: as the amount of training data increases, the training error may increase rather than decrease, leading to reduced decrease in prediction accuracy on new data. Our results highlight the limitations of amplitude encoding in QML and indicate that more efforts should be devoted to finding more efficient encoding strategies to unlock the full potential of QML.

quant-ph↗

arXiv2025

Realizing quantum optics in structured environments with giant atoms

To go beyond quantum optics in free-space setups, atom-light interfaces with structured photonic environments are often employed to realize unconventional quantum electrodynamics (QED) phenomena. However, when employed as quantum buses, those long-distance nanostructures are limited by fabrication disorders. In this work, we alternatively propose to realize structured lightmatter interactions by engineering multiple coupling points of hybrid giant atom-conventionalenvironments without any periodic structure. We present a generic optimization method to obtain the real-space coupling sequence for multiple coupling points. We report a broadband chiral emission in a very wide frequency regime, with no analog in other quantum setups. Moreover, we show that the QED phenomena in the band gap environment, such as fractional atomic decay and dipole-dipole interactions mediated by a bound state, can be observed in our setup. Numerical results indicate that our proposal is robust against fabrication disorders of the coupling sequence. Our work opens up a new route for realizing unconventional light-matter interactions.

quant-ph↗

arXiv2025

Exploring experimental limit of deep quantum signal processing using a trapped-ion simulator

Quantum signal processing (QSP), which enables systematic polynomial transformations on quantum data through sequences of qubit rotations, has emerged as a fundamental building block for quantum algorithms and data re-uploading quantum neural networks. While recent experiments have demonstrated the feasibility of shallow QSP circuits, the inherent limitations in scaling QSP to achieve complex transformations on quantum hardware remain an open and critical question. Here we report the first experimental realization of deep QSP circuits in a trapped-ion quantum simulator. By manipulating the qubit encoded in a trapped $^{43}\textrm{Ca}^{+}$ ion, we demonstrate high-precision simulation of some prominent functions used in quantum algorithms and machine learning, with circuit depths ranging from 15 to 360 layers and implementation time significantly longer than coherence time of the qubit. Our results reveal a crucial trade-off between the precision of function simulation and the concomitant accumulation of hardware noise, highlighting the importance of striking a balance between circuit depth and accuracy in practical QSP implementation. This work addresses a key gap in understanding the scalability and limitations of QSP-based algorithms on quantum hardware, providing valuable insights for developing quantum algorithms as well as practically realizing quantum singular value transformation and data re-uploading quantum machine learning models.

quant-ph↗

arXiv2025

Experimental Observation of Topological Disclination States in Lossy Electric Circuits

Topological phase transitions can be remarkably induced purely by manipulating gain and loss mechanisms, offering a novel approach to engineering topological properties. Recent theoretical studies have revealed gain-loss-induced topological disclination states, along with the associated fractional charge trapped at the disclination sites. Here, we present the experimental demonstration of topological disclination states in a purely lossy electric circuit. By designing alternating lossy electric circuit networks that correspond to the disclination lattice, we observe a voltage response localized at the disclination sites and demonstrate the robustness of these states against disorder. Furthermore, we measure the charge distribution, confirming the presence of fractional charge at the disclination sites, which gives rise to the topological disclination states. Our experiment provides direct evidence of gain-loss-induced topological disclination states in electric circuits, opening new possibilities for applications in classical systems.

cond-mat.mes-hall↗

arXiv2025

The JWST/PASSAGE Survey: Testing Reionization Histories with JWST's First Unbiased Survey for Lyman alpha Emitters at Redshifts 7.5-9.5

Lyman $α$ (Ly$α$) emission is one of few observable features of galaxies that can trace the neutral hydrogen content in the Universe during the Epoch of Reionization (EoR). To accomplish this we need an efficient way to survey for Ly$α$ emitters (LAEs) at redshifts beyond 7, requiring unbiased emission-line observations that are both sufficiently deep and wide to cover enough volume to detect them. Here we present results from PASSAGE -- a pure-parallel JWST/NIRISS slitless spectroscopic survey to detect Ly$α$ emitters deep into the EoR, without the bias of photometric preselection. We identify four LAEs at $7.5\leq z\leq9.5$ in four surveyed pointings, and estimate the luminosity function (LF). We find that the LF does show a marked decrease compared to post-reionization measurements, but the change is a factor of $\lesssim 10$, which is less than expected from theoretical calculations and simulations, as well as observational expectations from the pre-JWST literature. Modeling of the IGM and expected \lya\ profiles implies these galaxies reside in ionized bubbles of $\gtrapprox 2$ physical Mpc. We also report that in the four fields we detect {3,1,0,0} LAEs, which could indicate strong field-to-field variation in the LAE distribution, consistent with a patchy HI distribution at $z\sim8$. We compare the recovered LAE number counts with expectations from simulations and discuss the potential implications for reionization and its morphology.

astro-ph.GA↗

arXiv2025

Neural Radiance Fields in Medical Imaging: A Survey

Neural Radiance Fields (NeRF), as a pioneering technique in computer vision, offer great potential to revolutionize medical imaging by synthesizing three-dimensional representations from the projected two-dimensional image data. However, they face unique challenges when applied to medical applications. This paper presents a comprehensive examination of applications of NeRFs in medical imaging, highlighting four imminent challenges, including fundamental imaging principles, inner structure requirement, object boundary definition, and color density significance. We discuss current methods on different organs and discuss related limitations. We also review several datasets and evaluation metrics and propose several promising directions for future research.

eess.IV↗

arXiv2025

Parameterized quantum comb and simpler circuits for reversing unknown qubit-unitary operations

Quantum combs play a vital role in characterizing and transforming quantum processes, with wide-ranging applications in quantum information processing. However, obtaining the explicit quantum circuit for the desired quantum comb remains a challenging problem. We propose PQComb, a novel framework that employs parameterized quantum circuits (PQCs) or quantum neural networks to harness the full potential of quantum combs for diverse quantum process transformation tasks. This method is well-suited for near-term quantum devices and can be applied to various tasks in quantum machine learning. As a notable application, we present two streamlined protocols for the time-reversal simulation of unknown qubit unitary evolutions, reducing the ancilla qubit overhead from six to three compared to the previous best-known method. We also extend PQComb to solve the problems of qutrit unitary transformation and channel discrimination. Furthermore, we demonstrate the hardware efficiency and robustness of our qubit unitary inversion protocol under realistic noise simulations of IBM-Q superconducting quantum hardware, yielding a significant improvement in average similarity over the previous protocol under practical regimes. PQComb's versatility and potential for broader applications in quantum machine learning pave the way for more efficient and practical solutions to complex quantum tasks.

quant-ph↗

arXiv2025

Dominant Role of Coplanar Inflows in Driving Disk Evolution Revealed by Gas-Phase Metallicity Gradients

Using spatially resolved spectroscopic data from the MaNGA sample, we investigate the parameters influencing the radial gradients of gas-phase metallicity ($\nabla\log(\mathrm{O/H})$), to determine whether disk formation is primarily driven by coplanar gas inflow or by the independent evolution of distinct regions within the disk. Our results show that $\nabla \log(\mathrm{O/H})$ strongly correlates with local gas-phase metallicity at a given stellar mass, with steeper gradients observed in metal-poorer disks. This trend supports the coplanar gas inflow scenario, wherein the gas is progressively enriched by in situ star formation as it flows inward. In contrast, the radial gradient of stellar mass surface density shows very weak correlations with $\nabla \log(\mathrm{O/H})$, which is inconsistent with the independent evolution mode, where gas inflow, star formation, and metal enrichment occur independently within each annulus of the disk. Furthermore, we find that $\nabla \log(\mathrm{O/H})$ is also closely correlated with an indicator of local gas turbulence $σ_{\mathrm{gas}}/R_{\mathrm{e}}$, highlighting the competing roles of turbulence and coplanar inflow in shaping metallicity gradients. Our results provide indirect observational evidence supporting coplanar gas inflow as the driving mechanism for disk evolution.

astro-ph.GA↗

arXiv2025

RemInD: Remembering Anatomical Variations for Interpretable Domain Adaptive Medical Image Segmentation

This work presents a novel Bayesian framework for unsupervised domain adaptation (UDA) in medical image segmentation. While prior works have explored this clinically significant task using various strategies of domain alignment, they often lack an explicit and explainable mechanism to ensure that target image features capture meaningful structural information. Besides, these methods are prone to the curse of dimensionality, inevitably leading to challenges in interpretability and computational efficiency. To address these limitations, we propose RemInD, a framework inspired by human adaptation. RemInD learns a domain-agnostic latent manifold, characterized by several anchors, to memorize anatomical variations. By mapping images onto this manifold as weighted anchor averages, our approach ensures realistic and reliable predictions. This design mirrors how humans develop representative components to understand images and then retrieve component combinations from memory to guide segmentation. Notably, model prediction is determined by two explainable factors: a low-dimensional anchor weight vector, and a spatial deformation. This design facilitates computationally efficient and geometry-adherent adaptation by aligning weight vectors between domains on a probability simplex. Experiments on two public datasets, encompassing cardiac and abdominal imaging, demonstrate the superiority of RemInD, which achieves state-of-the-art performance using a single alignment approach, outperforming existing methods that often rely on multiple complex alignment strategies.

cs.CV↗

arXiv2025

Exploring Active Data Selection Strategies for Continuous Training in Deepfake Detection

In deepfake detection, it is essential to maintain high performance by adjusting the parameters of the detector as new deepfake methods emerge. In this paper, we propose a method to automatically and actively select the small amount of additional data required for the continuous training of deepfake detection models in situations where deepfake detection models are regularly updated. The proposed method automatically selects new training data from a \textit{redundant} pool set containing a large number of images generated by new deepfake methods and real images, using the confidence score of the deepfake detection model as a metric. Experimental results show that the deepfake detection model, continuously trained with a small amount of additional data automatically selected and added to the original training set, significantly and efficiently improved the detection performance, achieving an EER of 2.5% with only 15% of the amount of data in the pool set.

cs.CV↗

arXiv2025

ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals

Post-training quantization (PTQ) of large language models (LLMs) holds the promise in reducing the prohibitive computational cost at inference time. Quantization of all weight, activation and key-value (KV) cache tensors to 4-bit without significantly degrading generalizability is challenging, due to the high quantization error caused by extreme outliers in activations. To tackle this problem, we propose ResQ, a PTQ method that pushes further the state-of-the-art. By means of principal component analysis (PCA), it identifies a low-rank subspace (in practice 1/8 of the hidden dimension) in which activation variances are highest, and keep the coefficients within this subspace in high precision, e.g. 8-bit, while quantizing the rest to 4-bit. Within each subspace, invariant random rotation is applied to further suppress outliers. We show that this is a provably optimal mixed precision quantization scheme that minimizes error. With the Llama and Qwen2.5 families of models, we demonstrate that ResQ outperforms recent uniform and mixed precision PTQ methods on a variety of benchmarks, achieving up to 33\% lower perplexity on Wikitext than the next best method SpinQuant, and upto 3\times speedup over 16-bit baseline. Code is available at https://github.com/utkarsh-dmx/project-resq.

cs.LG↗

arXiv2025

Predicting symmetries of quantum dynamics with optimal samples

Identifying symmetries in quantum dynamics, such as identity or time-reversal invariance, is a crucial challenge with profound implications for quantum technologies. We introduce a unified framework combining group representation theory and subgroup hypothesis testing to predict these symmetries with optimal efficiency. By exploiting the inherent symmetry of compact groups and their irreducible representations, we derive an exact characterization of the optimal type-II error (failure probability to detect a symmetry), offering an operational interpretation for the quantum max-relative entropy. In particular, we prove that parallel strategies achieve the same performance as adaptive or indefinite-causal-order protocols, resolving debates about the necessity of complex control sequences. Applications to the singleton group, maximal commutative group, and orthogonal group yield explicit results: for predicting the identity property, Z-symmetry, and T-symmetry of unknown qubit unitaries, with zero type-I error and type-II error bounded by $δ$, we establish the explicit optimal sample complexity which scales as $\mathcal{O}(δ^{-1/3})$ for identity testing and $\mathcal{O}(δ^{-1/2})$ for T/Z-symmetry testing. These findings offer theoretical insights and practical guidelines for efficient unitary property testing and symmetry-driven protocols in quantum information processing.

quant-ph↗

arXiv2025

Overflow metabolism originates from growth optimization and cell heterogeneity

A classic problem in metabolism is that fast-proliferating cells use seemingly wasteful fermentation for energy biogenesis in the presence of sufficient oxygen. This counterintuitive phenomenon, known as overflow metabolism or the Warburg effect, is universal across various organisms. Despite extensive research, its origin and function remain unclear. Here, we show that overflow metabolism can be understood through growth optimization combined with cell heterogeneity. A model of optimal protein allocation, coupled with heterogeneity in enzyme catalytic rates among cells, quantitatively explains why and how cells choose between respiration and fermentation under different nutrient conditions. Our model quantitatively illustrates the growth rate dependence of fermentation flux and enzyme allocation under various perturbations and is fully validated by experimental results in Escherichia coli. Our work provides a quantitative explanation for the Crabtree effect in yeast and the Warburg effect in cancer cells and can be broadly used to address heterogeneity-related challenges in metabolism.

physics.bio-ph↗

arXiv2025

Optimizer-Dependent Generalization Bound for Quantum Neural Networks

Quantum neural networks (QNNs) play a pivotal role in addressing complex tasks within quantum machine learning, analogous to classical neural networks in deep learning. Ensuring consistent performance across diverse datasets is crucial for understanding and optimizing QNNs in both classical and quantum machine learning tasks, but remains a challenge as QNN's generalization properties have not been fully explored. In this paper, we investigate the generalization properties of QNNs through the lens of learning algorithm stability, circumventing the need to explore the entire hypothesis space and providing insights into how classical optimizers influence QNN performance. By establishing a connection between QNNs and quantum combs, we examine the general behaviors of QNN models from a quantum information theory perspective. Leveraging the uniform stability of the stochastic gradient descent algorithm, we propose a generalization error bound determined by the number of trainable parameters, data uploading times, dataset dimension, and classical optimizer hyperparameters. Numerical experiments validate this comprehensive understanding of QNNs and align with our theoretical conclusions. As the first exploration into understanding the generalization capability of QNNs from a unified perspective of design and training, our work offers practical insights for applying QNNs in quantum machine learning.

quant-ph↗

arXiv2025

Potential Indicator for Continuous Emotion Arousal by Dynamic Neural Synchrony

The need for automatic and high-quality emotion annotation is paramount in applications such as continuous emotion recognition and video highlight detection, yet achieving this through manual human annotations is challenging. Inspired by inter-subject correlation (ISC) utilized in neuroscience, this study introduces a novel Electroencephalography (EEG) based ISC methodology that leverages a single-electrode and feature-based dynamic approach. Our contributions are three folds. Firstly, we reidentify two potent emotion features suitable for classifying emotions-first-order difference (FD) an differential entropy (DE). Secondly, through the use of overall correlation analysis, we demonstrate the heterogeneous synchronized performance of electrodes. This performance aligns with neural emotion patterns established in prior studies, thus validating the effectiveness of our approach. Thirdly, by employing a sliding window correlation technique, we showcase the significant consistency of dynamic ISCs across various features or key electrodes in each analyzed film clip. Our findings indicate the method's reliability in capturing consistent, dynamic shared neural synchrony among individuals, triggered by evocative film stimuli. This underscores the potential of our approach to serve as an indicator of continuous human emotion arousal. The implications of this research are significant for advancements in affective computing and the broader neuroscience field, suggesting a streamlined and effective tool for emotion analysis in real-world applications.

eess.SP↗

arXiv2025

Riemannian Optimization for Holevo Capacity

Computing the classical capacity of a noisy quantum channel is crucial for understanding the limits of communication over quantum channels. However, its evaluation remains challenging due to the difficulty of computing the Holevo capacity and the even greater difficulty of regularization. In this work, we formulate the computation of the Holevo capacity as an optimization problem on a product manifold constructed from probability distributions and their corresponding pure input states for a quantum channel. A Riemannian gradient descent algorithm is proposed to solve the problem, providing lower bounds on the classical capacity of general quantum channels and outperforming existing methods in numerical experiments in both efficiency and scale.

quant-ph↗

arXiv2025

LLM-MedQA: Enhancing Medical Question Answering through Case Studies in Large Language Models

Accurate and efficient question-answering systems are essential for delivering high-quality patient care in the medical field. While Large Language Models (LLMs) have made remarkable strides across various domains, they continue to face significant challenges in medical question answering, particularly in understanding domain-specific terminologies and performing complex reasoning. These limitations undermine their effectiveness in critical medical applications. To address these issues, we propose a novel approach incorporating similar case generation within a multi-agent medical question-answering (MedQA) system. Specifically, we leverage the Llama3.1:70B model, a state-of-the-art LLM, in a multi-agent architecture to enhance performance on the MedQA dataset using zero-shot learning. Our method capitalizes on the model's inherent medical knowledge and reasoning capabilities, eliminating the need for additional training data. Experimental results show substantial performance gains over existing benchmark models, with improvements of 7% in both accuracy and F1-score across various medical QA tasks. Furthermore, we examine the model's interpretability and reliability in addressing complex medical queries. This research not only offers a robust solution for medical question answering but also establishes a foundation for broader applications of LLMs in the medical domain.

cs.CL↗