arXiv subjects

Xin Zhang

Publications and source records attributed to Xin Zhang.

At least 397 records · Page 22Linked to original sources

arXiv2025

Configuration Requirements for 21-cm Forest Background Quasar Searches with the Moon-based Interferometer

The 21-cm forest offers a powerful cosmological probe of the thermal history and small-scale structure of the intergalactic medium during the Epoch of Reionization (EoR). Its success, however, critically depends on the availability of high-redshift radio-loud quasars (HzRLQs) as background sources. In this work, we investigate the configuration requirements for a Moon-based low-frequency radio interferometer aimed at maximizing the detection of HzRLQs for future 21-cm forest studies. Building upon a previously developed quasar luminosity function (QLF), we forecast HzRLQ abundances under various array configurations. Assuming a total survey area of $10^4\,\mathrm{deg}^2$ and 1 year of observation, we compare continuum surveys with 10 MHz bandwidth and 21-cm forest surveys with 5 kHz resolution. Our results show that a minimum collecting area of $\sim$6 500 m$^2$ enables detection at $z \sim 6$, while SKA-like arrays ($N_{\mathrm{st}} = 512$) extend the detection limit to $z \sim 10$ for 21-cm forest survey and $z \sim 16$ for continuum survey. Larger arrays with $N_{\mathrm{st}} = 2048$ can reach $z \sim 11$ in 21-cm forest mode. We also explore configurations that maintain fixed collecting areas while increasing the number to enhance survey efficiency. This boosts source detection but significantly increases the data volume and computational demands. These results underscore the importance of optimizing array design for different survey goals and balancing sensitivity, spectral resolution, and data management. A well-designed Moon-based array could open a new observational window on reionization and early cosmic structure formation.

astro-ph.CO↗

arXiv2025

Exploring HI Galaxy Redshift Survey Strategies for the FAST Core Array Interferometry

We explore the feasibility of HI galaxy redshift surveys with the Five-hundred-meter Aperture Spherical Telescope (FAST) and its proposed Core Array interferometry. Using semi-analytical simulations, we compare the performance of the FAST single-dish and Core Array modes in drift scan (DS) and on-the-fly (OTF) observations across different redshifts. Our results show that the FAST single-dish mode enables significant HI detections at low redshifts ($z \lesssim 0.35$) but is limited at higher redshifts due to shot noise. The Core Array interferometry, with higher sensitivity and angular resolution, provides robust HI galaxy detections up to $z \sim 1$, maintaining a sufficient number density for power spectrum measurements and BAO constraints. At low redshifts ($z \sim 0.01$ -- $0.08$), both configurations perform well, though cosmic variance dominates uncertainties. At higher redshifts ($z > 0.35$), the Core Array outperforms the single-dish mode, while increasing the survey area has little impact on single-dish observations due to shot noise limitations. The DS mode efficiently covers large sky areas but is constrained by Earth's rotation, whereas the OTF mode allows more flexible deep-field surveys at the cost of operational overhead. Our findings highlight the importance of optimizing survey strategies to maximize FAST's potential for HI cosmology. The Core Array is particularly well-suited for high-redshift HI galaxy surveys, enabling precise constraints on large-scale structure and dark energy.

astro-ph.CO↗

arXiv2025

Deep Learning for Taxol Exposure Analysis: A New Cell Image Dataset and Attention-Based Baseline Model

Monitoring the effects of the chemotherapeutic agent Taxol at the cellular level is critical for both clinical evaluation and biomedical research. However, existing detection methods require specialized equipment, skilled personnel, and extensive sample preparation, making them expensive, labor-intensive, and unsuitable for high-throughput or real-time analysis. Deep learning approaches have shown great promise in medical and biological image analysis, enabling automated, high-throughput assessment of cellular morphology. Yet, no publicly available dataset currently exists for automated morphological analysis of cellular responses to Taxol exposure. To address this gap, we introduce a new microscopy image dataset capturing C6 glioma cells treated with varying concentrations of Taxol. To provide an effective solution for Taxol concentration classification and establish a benchmark for future studies on this dataset, we propose a baseline model named ResAttention-KNN, which combines a ResNet-50 with Convolutional Block Attention Modules and uses a k-Nearest Neighbors classifier in the learned embedding space. This model integrates attention-based refinement and non-parametric classification to enhance robustness and interpretability. Both the dataset and implementation are publicly released to support reproducibility and facilitate future research in vision-based biomedical analysis.

cs.CV↗

arXiv2025

Toward High-Precision Astrometry with CSST Using Multi-Gaussian Fitting of PSF

The Chinese Space Station Survey Telescope (CSST) presents significant potential for high-precision astrometry. In this study, we show that the point spread function (PSF) modeled by the discrete PSF with Multi-Gaussian function can effectively enhance the astrometric accuracy. We determine that the PSF profile can be accurately modeled by three Gaussians, which takes advantage of reduced computational complexity in PSF convolution. In sparse star fields, the lowest centering accuracy we obtain after aberration correction can be below 1 mas. We find that the proper motion errors remain below 1.0 mas/yr for point sources with five observations and approximately 0.8 mas/yr for seven observations with a time baseline of around 3.5 years. We finally demonstrate that the precision of our position measurements for stars fainter than 21 mag in the simulated CSST crowded field is better than the results from both SExtractor and DOLPHOT.

astro-ph.IM↗

arXiv2025

AEGIS: Authenticity Evaluation Benchmark for AI-Generated Video Sequences

Recent advances in AI-generated content have fueled the rise of highly realistic synthetic videos, posing severe risks to societal trust and digital integrity. Existing benchmarks for video authenticity detection typically suffer from limited realism, insufficient scale, and inadequate complexity, failing to effectively evaluate modern vision-language models against sophisticated forgeries. To address this critical gap, we introduce AEGIS, a novel large-scale benchmark explicitly targeting the detection of hyper-realistic and semantically nuanced AI-generated videos. AEGIS comprises over 10,000 rigorously curated real and synthetic videos generated by diverse, state-of-the-art generative models, including Stable Video Diffusion, CogVideoX-5B, KLing, and Sora, encompassing open-source and proprietary architectures. In particular, AEGIS features specially constructed challenging subsets enhanced with robustness evaluation. Furthermore, we provide multimodal annotations spanning Semantic-Authenticity Descriptions, Motion Features, and Low-level Visual Features, facilitating authenticity detection and supporting downstream tasks such as multimodal fusion and forgery localization. Extensive experiments using advanced vision-language models demonstrate limited detection capabilities on the most challenging subsets of AEGIS, highlighting the dataset's unique complexity and realism beyond the current generalization capabilities of existing models. In essence, AEGIS establishes an indispensable evaluation benchmark, fundamentally advancing research toward developing genuinely robust, reliable, broadly generalizable video authenticity detection methodologies capable of addressing real-world forgery threats. Our dataset is available on https://huggingface.co/datasets/Clarifiedfish/AEGIS.

cs.CV↗

arXiv2025

Few-Shot Deployment of Pretrained MRI Transformers in Brain Imaging Tasks

Machine learning using transformers has shown great potential in medical imaging, but its real-world applicability remains limited due to the scarcity of annotated data. In this study, we propose a practical framework for the few-shot deployment of pretrained MRI transformers in diverse brain imaging tasks. By utilizing the Masked Autoencoder (MAE) pretraining strategy on a large-scale, multi-cohort brain MRI dataset comprising over 31 million slices, we obtain highly transferable latent representations that generalize well across tasks and datasets. For high-level tasks such as classification, a frozen MAE encoder combined with a lightweight linear head achieves state-of-the-art accuracy in MRI sequence identification with minimal supervision. For low-level tasks such as segmentation, we propose MAE-FUnet, a hybrid architecture that fuses multiscale CNN features with pretrained MAE embeddings. This model consistently outperforms other strong baselines in both skull stripping and multi-class anatomical segmentation under data-limited conditions. With extensive quantitative and qualitative evaluations, our framework demonstrates efficiency, stability, and scalability, suggesting its suitability for low-resource clinical environments and broader neuroimaging applications.

cs.CV↗

arXiv2025

Two-dimensional Sparse Parallelism for Large Scale Deep Learning Recommendation Model Training

The increasing complexity of deep learning recommendation models (DLRM) has led to a growing need for large-scale distributed systems that can efficiently train vast amounts of data. In DLRM, the sparse embedding table is a crucial component for managing sparse categorical features. Typically, these tables in industrial DLRMs contain trillions of parameters, necessitating model parallelism strategies to address memory constraints. However, as training systems expand with massive GPUs, the traditional fully parallelism strategies for embedding table post significant scalability challenges, including imbalance and straggler issues, intensive lookup communication, and heavy embedding activation memory. To overcome these limitations, we propose a novel two-dimensional sparse parallelism approach. Rather than fully sharding tables across all GPUs, our solution introduces data parallelism on top of model parallelism. This enables efficient all-to-all communication and reduces peak memory consumption. Additionally, we have developed the momentum-scaled row-wise AdaGrad algorithm to mitigate performance losses associated with the shift in training paradigms. Our extensive experiments demonstrate that the proposed approach significantly enhances training efficiency while maintaining model performance parity. It achieves nearly linear training speed scaling up to 4K GPUs, setting a new state-of-the-art benchmark for recommendation model training.

cs.DC↗

arXiv2025

Physics-Embedded Neural ODEs for Sim2Real Edge Digital Twins of Hybrid Power Electronics Systems

Edge Digital Twins (EDTs) are crucial for monitoring and control of Power Electronics Systems (PES). However, existing modeling approaches struggle to consistently capture continuously evolving hybrid dynamics that are inherent in PES, degrading Sim-to-Real generalization on resource-constrained edge devices. To address these challenges, this paper proposes a Physics-Embedded Neural ODEs (PENODE) that (i) embeds the hybrid operating mechanism as an event automaton to explicitly govern discrete switching and (ii) injects known governing ODE components directly into the neural parameterization of unmodeled dynamics. This unified design yields a differentiable end-to-end trainable architecture that preserves physical interpretability while reducing redundancy, and it supports a cloud-to-edge toolchain for efficient FPGA deployment. Experimental results demonstrate that PENODE achieves significantly higher accuracy in benchmarks in white-box, gray-box, and black-box scenarios, with a 75% reduction in neuron count, validating that the proposed PENODE maintains physical interpretability, efficient edge deployment, and real-time control enhancement.

cs.LG↗

arXiv2025

Mitigation of exchange cross-talk in dense quantum dot arrays

Coupled spins in semiconductor quantum dots are a versatile platform for quantum computing and simulations of complex many-body phenomena. However, on the path of scale-up, cross-talk from densely packed electrodes poses a severe challenge. While cross-talk onto the dot potentials is nowadays routinely compensated for, cross-talk on the exchange interaction is much more difficult to tackle because it is not always directly measurable. Here we propose and implement a way of characterizing and compensating cross-talk on adjacent exchange interactions by following the singlet-triplet avoided crossing in Ge. We show that we can easily identify the barrier-to-barrier cross-talk element without knowledge of the particular exchange value in a 2x4 quantum dot array. We uncover striking differences among these cross-talk elements which can be linked to the geometry of the device and the barrier gate fan-out. We validate the methodology by tuning up four-spin Heisenberg chains. The same methodology should be applicable to longer chains of spins and to other semiconductor platforms in which mixing of the singlet and the lowest-energy triplet is present or can be engineered. Additionally, this procedure is well suited for automated tuning routines as we obtain a stand-out feature that can be easily tracked and directly returns the magnitude of the cross-talk.

cond-mat.mes-hall↗

arXiv2025

Neural Shell Texture Splatting: More Details and Fewer Primitives

Gaussian splatting techniques have shown promising results in novel view synthesis, achieving high fidelity and efficiency. However, their high reconstruction quality comes at the cost of requiring a large number of primitives. We identify this issue as stemming from the entanglement of geometry and appearance in Gaussian Splatting. To address this, we introduce a neural shell texture, a global representation that encodes texture information around the surface. We use Gaussian primitives as both a geometric representation and texture field samplers, efficiently splatting texture features into image space. Our evaluation demonstrates that this disentanglement enables high parameter efficiency, fine texture detail reconstruction, and easy textured mesh extraction, all while using significantly fewer primitives.

cs.GR↗

arXiv2025

LaMAGIC2: Advanced Circuit Formulations for Language Model-Based Analog Topology Generation

Automation of analog topology design is crucial due to customized requirements of modern applications with heavily manual engineering efforts. The state-of-the-art work applies a sequence-to-sequence approach and supervised finetuning on language models to generate topologies given user specifications. However, its circuit formulation is inefficient due to O(|V |2) token length and suffers from low precision sensitivity to numeric inputs. In this work, we introduce LaMAGIC2, a succinct float-input canonical formulation with identifier (SFCI) for language model-based analog topology generation. SFCI addresses these challenges by improving component-type recognition through identifier-based representations, reducing token length complexity to O(|V |), and enhancing numeric precision sensitivity for better performance under tight tolerances. Our experiments demonstrate that LaMAGIC2 achieves 34% higher success rates under a tight tolerance of 0.01 and 10X lower MSEs compared to a prior method. LaMAGIC2 also exhibits better transferability for circuits with more vertices with up to 58.5% improvement. These advancements establish LaMAGIC2 as a robust framework for analog topology generation.

cs.LG↗

arXiv2025

Large Language Model Agent for Structural Drawing Generation Using ReAct Prompt Engineering and Retrieval Augmented Generation

Structural drawings are widely used in many fields, e.g., mechanical engineering, civil engineering, etc. In civil engineering, structural drawings serve as the main communication tool between architects, engineers, and builders to avoid conflicts, act as legal documentation, and provide a reference for future maintenance or evaluation needs. They are often organized using key elements such as title/subtitle blocks, scales, plan views, elevation view, sections, and detailed sections, which are annotated with standardized symbols and line types for interpretation by engineers and contractors. Despite advances in software capabilities, the task of generating a structural drawing remains labor-intensive and time-consuming for structural engineers. Here we introduce a novel generative AI-based method for generating structural drawings employing a large language model (LLM) agent. The method incorporates a retrieval-augmented generation (RAG) technique using externally-sourced facts to enhance the accuracy and reliability of the language model. This method is capable of understanding varied natural language descriptions, processing these to extract necessary information, and generating code to produce the desired structural drawing in AutoCAD. The approach developed, demonstrated and evaluated herein enables the efficient and direct conversion of a structural drawing's natural language description into an AutoCAD drawing, significantly reducing the workload compared to current working process associated with manual drawing production, facilitating the typical iterative process of engineers for expressing design ideas in a simplified way.

cs.LG↗

arXiv2025

Exact spin helix eigenstates in the anisotropic spin-$s$ Heisenberg model with arbitrary dimensions

Spin helix states-characterized by their spatially modulated spin textures-are exact eigenstates of the one-dimensional anisotropic spin-$\frac{1}{2}$ Heisenberg model under specific parameter conditions. In this work, we extend this framework by constructing exact spin helix eigenstates for the fully anisotropic XYZ Heisenberg model with arbitrary spatial dimensions and arbitrary spin quantum numbers. Our results demonstrate that some non-trivial exact eigenstates can persist beyond the integrable regime. The XXZ and XY cases are also analyzed to clarify the conditions for the emergence of spin helix eigenstates and their key properties. Our results broaden the class of analytically tractable exact eigenstates in non-integrable systems and deepen understanding of spin modulation states in many-body systems.

math-ph↗

arXiv2025

From Flat to Round: Redefining Brain Decoding with Surface-Based fMRI and Cortex Structure

Reconstructing visual stimuli from human brain activity (e.g., fMRI) bridges neuroscience and computer vision by decoding neural representations. However, existing methods often overlook critical brain structure-function relationships, flattening spatial information and neglecting individual anatomical variations. To address these issues, we propose (1) a novel sphere tokenizer that explicitly models fMRI signals as spatially coherent 2D spherical data on the cortical surface; (2) integration of structural MRI (sMRI) data, enabling personalized encoding of individual anatomical variations; and (3) a positive-sample mixup strategy for efficiently leveraging multiple fMRI scans associated with the same visual stimulus. Collectively, these innovations enhance reconstruction accuracy, biological interpretability, and generalizability across individuals. Experiments demonstrate superior reconstruction performance compared to SOTA methods, highlighting the effectiveness and interpretability of our biologically informed approach.

cs.CV↗

arXiv2025

Constraining parity and Lorentz violations in gravity with future ground- and space-based gravitational wave detectors

The future ground- and space-based gravitational wave (GW) detectors offer unprecedented opportunities to test general relativity (GR) with greater precision. In this work, we investigate the capability of future ground-based GW detectors, the Einstein Telescope (ET) and the Cosmic Explorer (CE), and space-based GW detectors, LISA, Taiji, and TianQin, for constraining parity and Lorentz violations in gravity. We inject several typical GW signals from compact binary systems into GW detectors and perform Bayesian inferences with the modified waveforms with parity and Lorentz-violating effects. These effects are modeled in the amplitude and phase corrections to the GW waveforms with their frequency-dependence described by factors $β_ν$, $β_μ$, $β_{\bar ν}$, and $β_{\bar μ}$. Our results show that the combined observations of ET and CE will impose significantly tighter bounds on the energy scale of parity and Lorentz violations ($M_{\rm PV}$ and $M_{\rm LV}$) compared to those given by LIGO-Virgo-KAGRA (LVK) detectors. For cases with positive values of $β_ν$, $β_μ$, $β_{\bar ν}$, and $β_{\bar μ}$, the constraints on $M_{\rm PV}$ and $M_{\rm LV}$ from ground-based detectors are tighter than those from the space-based detectors. For the $β_μ = -1$ case, space-based GW detectors provide constraints on $M_{\rm PV}$ that are better than current LVK observations and comparable to those from ET and CE. Additionally, space-based detectors exhibit superior sensitivity in constraining $M_{\rm LV}$ for $β_{\bar μ} = -2$ case, which is approximately three orders of magnitude tighter than those from ground-based GW detectors. This scenario also enables bounds on the graviton mass at $m_g \lesssim 10^{-35}\; {\rm GeV}$. These findings highlight the promising role of future GW observatories in probing fundamental physics beyond GR.

gr-qc↗

arXiv2025

Prospects for probing dark matter particles and primordial black holes with the Hongmeng mission using the 21 cm global spectrum at cosmic dawn

Probing dark matter particles and primordial black holes remains a pivotal challenge in modern cosmology. Exotic energy injections from dark matter annihilation, decay, and PBH Hawking evaporation can alter the thermal and ionization histories of the early universe, leaving distinctive imprints on the 21 cm global spectrum. We assess the potential of the upcoming space project, the Hongmeng mission, to probe dark matter particles and PBHs using the 21 cm global spectrum. Under ideal conditions with 1000 hours of integration time and negligible foreground residuals, the Hongmeng project can reach sensitivities to dark matter annihilation cross sections and decay lifetimes to $\langle σv \rangle \sim 10^{-28}\,\mathrm{cm^3\,s^{-1}}$ and $τ\sim 10^{28}\,\mathrm{s}$, respectively, for dark matter particles with a mass of $10\,\mathrm{GeV}$. It can also probe PBHs with masses of $10^{16}\,\mathrm{g}$ and abundances as low as $f_{\mathrm{PBH}} \simeq 10^{-6}$. These results indicate that the Hongmeng mission can improve current constraints on dark matter annihilation, decay, and PBH Hawking radiation by nearly two orders of magnitude. Moreover, the Hongmeng mission surpasses current limits on sub-GeV dark matter probing and enables the probing of Hawking radiation from PBHs with masses above $10^{17}\,\mathrm{g}$, which remain undetectable through conventional cosmological means. Overall, the upcoming Hongmeng project holds great promise for advancing the investigation of both dark matter and PBHs, potentially deepening our understanding of the nature of dark matter.

astro-ph.CO↗

arXiv2025

External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation

Ads recommendation is a prominent service of online advertising systems and has been actively studied. Recent studies indicate that scaling-up and advanced design of the recommendation model can bring significant performance improvement. However, with a larger model scale, such prior studies have a significantly increasing gap from industry as they often neglect two fundamental challenges in industrial-scale applications. First, training and inference budgets are restricted for the model to be served, exceeding which may incur latency and impair user experience. Second, large-volume data arrive in a streaming mode with data distributions dynamically shifting, as new users/ads join and existing users/ads leave the system. We propose the External Large Foundation Model (ExFM) framework to address the overlooked challenges. Specifically, we develop external distillation and a data augmentation system (DAS) to control the computational cost of training/inference while maintaining high performance. We design the teacher in a way like a foundation model (FM) that can serve multiple students as vertical models (VMs) to amortize its building cost. We propose Auxiliary Head and Student Adapter to mitigate the data distribution gap between FM and VMs caused by the streaming data issue. Comprehensive experiments on internal industrial-scale applications and public datasets demonstrate significant performance gain by ExFM.

cs.IR↗

arXiv2025

Exploring non-cold dark matter in a scenario of dynamical dark energy with DESI DR2 data

Recent observations of DESI hint that dark matter (DM) may not be cold but have a non-zero equation of state (EoS) parameter, and that dark energy (DE) may not be a cosmological constant. In this work, we explore the possibility of a non-zero DM EoS parameter within the framework of dynamical DE. We perform analysis by using the latest baryon acoustic oscillation (BAO) data from DESI DR2, the cosmic microwave background (CMB) data from Planck, and the type Ia supernova (SN) data from DESY5 and PantheonPlus. When using the combination of CMB, BAO, and SN data, our results indicate a preference for a non-zero DM EoS parameter at the $2.8σ$ and $3.3σ$ level within the content of a constant DE EoS. In contrast, for a time-evolving DE EoS parameterized by $w_0$ and $w_a$, this preference decreases to $0.8σ$ and $1.1σ$. Furthermore, allowing a non-zero DM EoS yields best-fit values of $w_0$ and $w_a$ that exhibit smaller deviations from the $Λ$CDM expectations, and Bayesian evidence analysis shows a comparable preference for this model relative to $Λ$CDM. The overall results of this work indicate that a non-zero DM EoS parameter warrants further exploration and investigation.

astro-ph.CO↗