arXiv subjects

Xin Wang

Publications and source records attributed to Xin Wang.

At least 703 records · Page 39Linked to original sources

arXiv2025

Scalable Quantum Architecture Search via Landscape Analysis

Balancing trainability and expressibility is a central challenge in variational quantum computing, and quantum architecture search (QAS) plays a pivotal role by automatically designing problem-specific parameterized circuits that address this trade-off. In this work, we introduce a scalable, training-free QAS framework that efficiently explores and evaluates quantum circuits through landscape fluctuation analysis. This analysis captures key characteristics of the cost function landscape, enabling accurate prediction of circuit learnability without costly training. By combining this metric with a streamlined two-level search strategy, our approach identifies high-performance, large-scale circuits with higher accuracy and fewer gates. We further demonstrate the practicality and scalability of our method, achieving significantly lower classical resource consumption compared to prior work. Notably, our framework attains robust performance on a challenging 50-qubit quantum many-body simulation, highlighting its potential for addressing complex quantum problems.

quant-ph↗

arXiv2025

Computable and Faithful Lower Bound on Entanglement Cost

Quantifying the minimum entanglement needed to prepare quantum states and implement quantum processes is a key challenge in quantum information theory. In this work, we develop computable and faithful lower bounds on the entanglement cost under quantum operations that completely preserve the positivity of partial transpose (PPT operations), by introducing the generalized divergence of $k$-negativity, a generalization of logarithmic negativity. Our bounds are efficiently computable via semidefinite programming and provide non-trivial values for all states that are non-PPT (NPT), establishing their faithfulness for the resource theory of NPT entanglement. Notably, we find and affirm the irreversibility of asymptotic entanglement manipulation under PPT operations for full-rank entangled states. Furthermore, we extend our methodology to derive lower bounds on the entanglement cost of both point-to-point and bipartite quantum channels. Our bound demonstrates improvements over previously known computable bounds for a wide range of quantum states and channels. These findings push the boundaries of understanding the structure of entanglement and the fundamental limits of entanglement manipulation.

quant-ph↗

arXiv2025

Magnetization-resolved density of states and quasi-first order transition in the two-dimensional random bond Ising model: an entropic sampling study

Systems with quenched disorder possess complex energy landscapes that are challenging to explore under the conventional Monte Carlo method. In this work, we implement an efficient entropy sampling scheme for accurate computation of the entropy function in low-energy regions. The method is applied to the two-dimensional $\pm J$ random-bond Ising model, where frustration is controlled by the fraction $p$ of ferromagnetic bonds. We investigate the low-temperature paramagnetic--ferromagnetic phase boundary below the multicritical point at $T_N = 0.9530(4)$, $P_N = 0.89078(8)$, as well as the zero-temperature ferromagnetic--spin-glass transition. Finite-size scaling analysis reveals that the phase boundary for $T < T_N$ exhibits reentrant behavior. By analyzing the evolution of the magnetization-resolved density of states $g(E, M)$ and ground-state spin configurations against increasing frustration, we provide strong evidence that the zero-temperature transition is quasi-first order. Finite-size scaling conducted on the spin-glass side supports the validity of $β= 0$, with a correlation length exponent $ν= 1.50(8)$. Our results provide new insights into the nature of the ferromagnetic-to-spin-glass phase transition in an extensively degenerate ground state.

cond-mat.dis-nn↗

arXiv2025

RLMiniStyler: Light-weight RL Style Agent for Arbitrary Sequential Neural Style Generation

Arbitrary style transfer aims to apply the style of any given artistic image to another content image. Still, existing deep learning-based methods often require significant computational costs to generate diverse stylized results. Motivated by this, we propose a novel reinforcement learning-based framework for arbitrary style transfer RLMiniStyler. This framework leverages a unified reinforcement learning policy to iteratively guide the style transfer process by exploring and exploiting stylization feedback, generating smooth sequences of stylized results while achieving model lightweight. Furthermore, we introduce an uncertainty-aware multi-task learning strategy that automatically adjusts loss weights to adapt to the content and style balance requirements at different training stages, thereby accelerating model convergence. Through a series of experiments across image various resolutions, we have validated the advantages of RLMiniStyler over other state-of-the-art methods in generating high-quality, diverse artistic image sequences at a lower cost. Codes are available at https://github.com/fengxiaoming520/RLMiniStyler.

cs.CV↗

arXiv2025

Robust Fairness Vision-Language Learning for Medical Image Analysis

The advent of Vision-Language Models (VLMs) in medical image analysis has the potential to help process multimodal inputs and increase performance over traditional inference methods. However, when considering the domain in which these models will be implemented, fairness and robustness are important to ensure the model stays true for any patient. In this paper, we introduce a framework for ensuring robustness and fairness of VLM models. This framework modifies the loss function at training by identifying and adjusting faulty image-text pairs through a Dynamic Bad Pair Mining algorithm and also utilizing Sinkhorn distance to ensure the loss distributions of protected groups do not deviate from the total loss. Experimental testing of our framework shows up to a 8.6\% improvement when looking at equity-scaled AUC.

cs.CV↗

arXiv2025

Robust AI-Generated Face Detection with Imbalanced Data

Deepfakes, created using advanced AI techniques such as Variational Autoencoder and Generative Adversarial Networks, have evolved from research and entertainment applications into tools for malicious activities, posing significant threats to digital trust. Current deepfake detection techniques have evolved from CNN-based methods focused on local artifacts to more advanced approaches using vision transformers and multimodal models like CLIP, which capture global anomalies and improve cross-domain generalization. Despite recent progress, state-of-the-art deepfake detectors still face major challenges in handling distribution shifts from emerging generative models and addressing severe class imbalance between authentic and fake samples in deepfake datasets, which limits their robustness and detection accuracy. To address these challenges, we propose a framework that combines dynamic loss reweighting and ranking-based optimization, which achieves superior generalization and performance under imbalanced dataset conditions. The code is available at https://github.com/Purdue-M2/SP_CUP.

cs.CV↗

arXiv2025

MISE: Meta-knowledge Inheritance for Social Media-Based Stressor Estimation

Stress haunts people in modern society, which may cause severe health issues if left unattended. With social media becoming an integral part of daily life, leveraging social media to detect stress has gained increasing attention. While the majority of the work focuses on classifying stress states and stress categories, this study introduce a new task aimed at estimating more specific stressors (like exam, writing paper, etc.) through users' posts on social media. Unfortunately, the diversity of stressors with many different classes but a few examples per class, combined with the consistent arising of new stressors over time, hinders the machine understanding of stressors. To this end, we cast the stressor estimation problem within a practical scenario few-shot learning setting, and propose a novel meta-learning based stressor estimation framework that is enhanced by a meta-knowledge inheritance mechanism. This model can not only learn generic stressor context through meta-learning, but also has a good generalization ability to estimate new stressors with little labeled data. A fundamental breakthrough in our approach lies in the inclusion of the meta-knowledge inheritance mechanism, which equips our model with the ability to prevent catastrophic forgetting when adapting to new stressors. The experimental results show that our model achieves state-of-the-art performance compared with the baselines. Additionally, we construct a social media-based stressor estimation dataset that can help train artificial intelligence models to facilitate human well-being. The dataset is now public at \href{https://www.kaggle.com/datasets/xinwangcs/stressor-cause-of-mental-health-problem-dataset}{\underline{Kaggle}} and \href{https://huggingface.co/datasets/XinWangcs/Stressor}{\underline{Hugging Face}}.

cs.LG↗

arXiv2025

S-SYNC: Shuttle and Swap Co-Optimization in Quantum Charge-Coupled Devices

The Quantum Charge-Coupled Device (QCCD) architecture is a modular design to expand trapped-ion quantum computer that relies on the coherent shuttling of qubits across an array of segmented electrodes. Leveraging trapped ions for their long coherence times and high-fidelity quantum operations, QCCD technology represents a significant advancement toward practical, large-scale quantum processors. However, shuttling increases thermal motion and consistently necessitates qubit swaps, significantly extend execution time and negatively affect application success rates. In this paper, we introduce S-SYNC -- a compiler designed to co-optimize the number of shuttling and swapping operations. S-SYNC exploits the unique properties of QCCD and incorporates generic SWAP operations to efficiently manage shuttle and SWAP counts simultaneously. Building on the static topology formulation of QCCD, we develop scheduling heuristics to enhance overall performance. Our evaluations demonstrate that our approach reduces the shuttling number by 3.69x on average and improves the success rate of quantum applications by 1.73x on average. Moreover, we apply S-SYNC to gain insights into executing applications across various QCCD topologies and to compare the trade-offs between different initial mapping methods.

quant-ph↗

arXiv2025

Simultaneously detecting spatiotemporal changes with penalized Poisson regression models

In the realm of large-scale spatiotemporal data, abrupt changes are commonly occurring across both spatial and temporal domains. This study aims to address the concurrent challenges of detecting change points and identifying spatial clusters within spatiotemporal count data. We introduce an innovative method based on the Poisson regression model, employing doubly fused penalization to unveil the underlying spatiotemporal change patterns. To efficiently estimate the model, we present an iterative shrinkage and threshold based algorithm to minimize the doubly penalized likelihood function. We establish the statistical consistency properties of the proposed estimator, confirming its reliability and accuracy. Furthermore, we conduct extensive numerical experiments to validate our theoretical findings, thereby highlighting the superior performance of our method when compared to existing competitive approaches.

stat.ME↗

arXiv2025

GarmentX: Autoregressive Parametric Representations for High-Fidelity 3D Garment Generation

This work presents GarmentX, a novel framework for generating diverse, high-fidelity, and wearable 3D garments from a single input image. Traditional garment reconstruction methods directly predict 2D pattern edges and their connectivity, an overly unconstrained approach that often leads to severe self-intersections and physically implausible garment structures. In contrast, GarmentX introduces a structured and editable parametric representation compatible with GarmentCode, ensuring that the decoded sewing patterns always form valid, simulation-ready 3D garments while allowing for intuitive modifications of garment shape and style. To achieve this, we employ a masked autoregressive model that sequentially predicts garment parameters, leveraging autoregressive modeling for structured generation while mitigating inconsistencies in direct pattern prediction. Additionally, we introduce GarmentX dataset, a large-scale dataset of 378,682 garment parameter-image pairs, constructed through an automatic data generation pipeline that synthesizes diverse and high-quality garment images conditioned on parametric garment representations. Through integrating our method with GarmentX dataset, we achieve state-of-the-art performance in geometric fidelity and input image alignment, significantly outperforming prior approaches. We will release GarmentX dataset upon publication.

cs.CV↗

arXiv2025

The H2 Glow of a Quiescent Molecular Cloud Observed with JWST

We report JWST MIRI/MRS observations of the H2 S(1) 17.04 micron transition in two regions in the boundary of the Taurus Molecular Cloud. The two regions, denoted Edge (near the relatively sharp boundary of the 13CO J=1-0 emission) and Peak (the location of the strongest H2 emission observed with Spitzer), have average intensities of 14.5 MJy/sr and 32.1 MJy/sr, respectively. We find small scale structures of characteristic size 1.0 to 2.5 arcseconds, corresponding to 140 AU to 350 AU, with characteristic intensity above the extended background of 10 MJy/sr, corresponding to a J = 3 column density of 1.6x1017/cm2. The most plausible explanation for the observed intensities from level 845 K above the J = 1 ortho-H2 ground state level is excitation by collisions with H2 molecules (the hydrogen in this region being predominantly molecular). Two mechanisms, turbulent dissipation and shocks, have been proposed for heating of localized regions of the ISM to temperatures ~1000 K to explain abundances of and emission from particular molecules. While we cannot determine unique values of density and kinetic temperature, the solutions in best agreement with predictions of shock models are H2 density = 370 /cm3 and kinetic temperature = 1000 K. The total H2 column density of the small-scale structures under these conditions is ~8x1017/cm2. This first direct detection of heated tiny scale structure in the quiescent molecular interstellar medium has significant implications for the physical structure of this phase of the ISM and maintaining supersonic motions within it.

astro-ph.GA↗

arXiv2025

Constraints on the galaxy formation models during epoch of reionization with high redshift observations

We use high resolution N-body dark matter simulations and L-Galaxies semi-analytical galaxy formation models to explore the high-$z$ galaxy properties and estimate the budget of ionizing photons. The parameters within L-Galaxies are obtained using a Markov Chain Monte Carlo (MCMC) method with high-$z$ galaxy observations from JWST and other telescopes. We consider two versions of L-Galaxies with and without dust correction on galaxy UV luminosities. With the best-fit parameters, both L-Galaxies 2015 and L-Galaxies 2020 reproduce well observations of UV luminosity functions, stellar mass functions, star formation rate densities and ionizing photon emission efficiency. With the assumption of escape fraction of $20\%$, all models produce more ionizing photons than the number of Hydrogen atoms in the Universe at $z>6$. The inclusion of dust correction within MCMC results in higher star formation efficiency, which predicts $\sim 50\%$ more ionizing photons, with better consistency between the predicted stellar mass functions and observations.

astro-ph.CO↗

arXiv2025

NoiseController: Towards Consistent Multi-view Video Generation via Noise Decomposition and Collaboration

High-quality video generation is crucial for many fields, including the film industry and autonomous driving. However, generating videos with spatiotemporal consistencies remains challenging. Current methods typically utilize attention mechanisms or modify noise to achieve consistent videos, neglecting global spatiotemporal information that could help ensure spatial and temporal consistency during video generation. In this paper, we propose the NoiseController, consisting of Multi-Level Noise Decomposition, Multi-Frame Noise Collaboration, and Joint Denoising, to enhance spatiotemporal consistencies in video generation. In multi-level noise decomposition, we first decompose initial noises into scene-level foreground/background noises, capturing distinct motion properties to model multi-view foreground/background variations. Furthermore, each scene-level noise is further decomposed into individual-level shared and residual components. The shared noise preserves consistency, while the residual component maintains diversity. In multi-frame noise collaboration, we introduce an inter-view spatiotemporal collaboration matrix and an intra-view impact collaboration matrix , which captures mutual cross-view effects and historical cross-frame impacts to enhance video quality. The joint denoising contains two parallel denoising U-Nets to remove each scene-level noise, mutually enhancing video generation. We evaluate our NoiseController on public datasets focusing on video generation and downstream tasks, demonstrating its state-of-the-art performance.

cs.CV↗

arXiv2025

ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech

ASVspoof 5 is the fifth edition in a series of challenges which promote the study of speech spoofing and deepfake attacks as well as the design of detection solutions. We introduce the ASVspoof 5 database which is generated in a crowdsourced fashion from data collected in diverse acoustic conditions (cf. studio-quality data for earlier ASVspoof databases) and from ~2,000 speakers (cf. ~100 earlier). The database contains attacks generated with 32 different algorithms, also crowdsourced, and optimised to varying degrees using new surrogate detection models. Among them are attacks generated with a mix of legacy and contemporary text-to-speech synthesis and voice conversion models, in addition to adversarial attacks which are incorporated for the first time. ASVspoof 5 protocols comprise seven speaker-disjoint partitions. They include two distinct partitions for the training of different sets of attack models, two more for the development and evaluation of surrogate detection models, and then three additional partitions which comprise the ASVspoof 5 training, development and evaluation sets. An auxiliary set of data collected from an additional 30k speakers can also be used to train speaker encoders for the implementation of attack algorithms. Also described herein is an experimental validation of the new ASVspoof 5 database using a set of automatic speaker verification and spoof/deepfake baseline detectors. With the exception of protocols and tools for the generation of spoofed/deepfake speech, the resources described in this paper, already used by participants of the ASVspoof 5 challenge in 2024, are now all freely available to the community.

eess.AS↗

arXiv2025

A negative stellar mass$-$gaseous metallicity gradient relation of dwarf galaxies modulated by stellar feedback

Baryonic cycling is reflected in the spatial distribution of metallicity within galaxies, yet gas-phase metallicity distribution and its connection with other properties of dwarf galaxies are largely unexplored. We present the first systematic study of radial gradients of gas-phase metallicities for a sample of 55 normal nearby star-forming dwarf galaxies (stellar mass $M_\star$ ranging from $10^7$ to $10^{9.5}\ M_\odot$), based on MUSE spectroscopic observations. We find that metallicity gradient shows a significant negative correlation (correlation coefficient $r \approx -0.56$) with $\log M_\star$, in contrast to the flat or even positive correlation observed for higher-mass galaxies. This negative correlation is accompanied by a stronger central suppression of metallicity compared to the outskirts in lower-mass galaxies. Among the other explored galaxy properties-including baryonic mass, star formation distribution, galaxy environment, regularity of the gaseous velocity field, and effective yield of metals $y_{\rm eff}$-only the velocity field regularity and $y_{\rm eff}$ show residual correlation with the metallicity gradient after controlling for $M_\star$, in the sense that galaxies with irregular velocity fields or lower $y_{\rm eff}$ tend to have less negative or more positive gradients. Particularly, a linear combination of $\log M_\star$ and $\log y_{\rm eff}$ significantly improves the correlation with metallicity gradient ($r \approx -0.68$) compared to $\log M_\star$ alone. The lack of correlation with environment disfavors gas accretion as a dominant factor. Our findings imply that metal mixing and transport processes, including but not limited to feedback-driven outflows, are more important than in-situ metal production in shaping the metallicity distribution of dwarf galaxies.

astro-ph.GA↗

arXiv2025

Adapting General Disentanglement-Based Speaker Anonymization for Enhanced Emotion Preservation

A general disentanglement-based speaker anonymization system typically separates speech into content, speaker, and prosody features using individual encoders. This paper explores how to adapt such a system when a new speech attribute, for example, emotion, needs to be preserved to a greater extent. While existing systems are good at anonymizing speaker embeddings, they are not designed to preserve emotion. Two strategies for this are examined. First, we show that integrating emotion embeddings from a pre-trained emotion encoder can help preserve emotional cues, even though this approach slightly compromises privacy protection. Alternatively, we propose an emotion compensation strategy as a post-processing step applied to anonymized speaker embeddings. This conceals the original speaker's identity and reintroduces the emotional traits lost during speaker embedding anonymization. Specifically, we model the emotion attribute using support vector machines to learn separate boundaries for each emotion. During inference, the original speaker embedding is processed in two ways: one, by an emotion indicator to predict emotion and select the emotion-matched SVM accurately; and two, by a speaker anonymizer to conceal speaker characteristics. The anonymized speaker embedding is then modified along the corresponding SVM boundary towards an enhanced emotional direction to save the emotional cues. The proposed strategies are also expected to be useful for adapting a general disentanglement-based speaker anonymization system to preserve other target paralinguistic attributes, with potential for a range of downstream tasks.

cs.SD↗

arXiv2025

MAR-3D: Progressive Masked Auto-regressor for High-Resolution 3D Generation

Recent advances in auto-regressive transformers have revolutionized generative modeling across different domains, from language processing to visual generation, demonstrating remarkable capabilities. However, applying these advances to 3D generation presents three key challenges: the unordered nature of 3D data conflicts with sequential next-token prediction paradigm, conventional vector quantization approaches incur substantial compression loss when applied to 3D meshes, and the lack of efficient scaling strategies for higher resolution latent prediction. To address these challenges, we introduce MAR-3D, which integrates a pyramid variational autoencoder with a cascaded masked auto-regressive transformer (Cascaded MAR) for progressive latent upscaling in the continuous space. Our architecture employs random masking during training and auto-regressive denoising in random order during inference, naturally accommodating the unordered property of 3D latent tokens. Additionally, we propose a cascaded training strategy with condition augmentation that enables efficiently up-scale the latent token resolution with fast convergence. Extensive experiments demonstrate that MAR-3D not only achieves superior performance and generalization capabilities compared to existing methods but also exhibits enhanced scaling capabilities compared to joint distribution modeling approaches (e.g., diffusion transformers).

cs.CV↗

arXiv2025

Understanding the Difficulty of Low-Precision Post-Training Quantization for LLMs

Large language models of high parameter counts are computationally expensive, yet can be made much more efficient by compressing their weights to very low numerical precision. This can be achieved either through post-training quantization by minimizing local, layer-wise quantization errors, or through quantization-aware fine-tuning by minimizing the global loss function. In this study, we discovered that, under the same data constraint, the former approach nearly always fared worse than the latter, a phenomenon particularly prominent when the numerical precision is very low. We further showed that this difficulty of post-training quantization arose from stark misalignment between optimization of the local and global objective functions. Our findings explains limited utility in minimization of local quantization error and the importance of direct quantization-aware fine-tuning, in the regime of large models at very low precision.

cs.LG↗