arXiv subjects

Xin Zhang

Publications and source records attributed to Xin Zhang.

At least 451 records · Page 25Linked to original sources

arXiv2025

Dissipatively dressed quasiparticles in boundary driven integrable spin chains

The nonequilibrium steady state (NESS) of integrable spin chains experiencing strong boundary dissipation is accounted by introducing quasiparticles with a renormalized -- dissipatively dressed -- dispersion relation. This allows us to evaluate the spectrum of the NESS in terms of the Bethe ansatz equations for a related coherent system which has the same set of eigenstates, the so-called dissipation-projected Hamiltonian. We find explicit analytic expressions for the dressed energies of the XXX and XXZ models with effective, i.e., induced by the dissipation, diagonal boundary fields, which are U(1) invariant, as well as the XXZ and XYZ models with effective non-diagonal boundary fields. In all cases, the dissipative dressing generates an extra singularity in the dispersion relation, substantially altering the NESS spectrum with respect to the spectrum of the corresponding coherent model.

cond-mat.stat-mech↗

arXiv2025

Satellites Reveal Mobility: A Commuting Origin-destination Flow Generator for Global Cities

Commuting Origin-destination~(OD) flows, capturing daily population mobility of citizens, are vital for sustainable development across cities around the world. However, it is challenging to obtain the data due to the high cost of travel surveys and privacy concerns. Surprisingly, we find that satellite imagery, publicly available across the globe, contains rich urban semantic signals to support high-quality OD flow generation, with over 98\% expressiveness of traditional multisource hard-to-collect urban sociodemographic, economics, land use, and point of interest data. This inspires us to design a novel data generator, GlODGen, which can generate OD flow data for any cities of interest around the world. Specifically, GlODGen first leverages Vision-Language Geo-Foundation Models to extract urban semantic signals related to human mobility from satellite imagery. These features are then combined with population data to form region-level representations, which are used to generate OD flows via graph diffusion models. Extensive experiments on 4 continents and 6 representative cities show that GlODGen has great generalizability across diverse urban environments on different continents and can generate OD flow data for global cities highly consistent with real-world mobility data. We implement GlODGen as an automated tool, seamlessly integrating data acquisition and curation, urban semantic feature extraction, and OD flow generation together. It has been released at https://github.com/tsinghua-fib-lab/generate-od-pubtools.

cs.CV↗

arXiv2025

Velocitune: A Velocity-based Dynamic Domain Reweighting Method for Continual Pre-training

It is well-known that a diverse corpus is critical for training large language models, which are typically constructed from a mixture of various domains. In general, previous efforts resort to sampling training data from different domains with static proportions, as well as adjusting data proportions during training. However, few methods have addressed the complexities of domain-adaptive continual pre-training. To fill this gap, we propose Velocitune, a novel framework dynamically assesses learning velocity and adjusts data proportions accordingly, favoring slower-learning domains while shunning faster-learning ones, which is guided by a scaling law to indicate the desired learning goal for each domain with less associated cost. To evaluate the effectiveness of Velocitune, we conduct experiments in a reasoning-focused dataset with CodeLlama, as well as in a corpus specialised for system command generation with Llama3 and Mistral. Velocitune achieves performance gains in both math and code reasoning tasks and command-line generation benchmarks. Further analysis reveals that key factors driving Velocitune's effectiveness include target loss prediction and data ordering.

cs.CL↗

arXiv2025

Deep learning-driven likelihood-free parameter inference for 21-cm forest observations

The hyperfine structure absorption lines of neutral hydrogen in spectra of high-redshift radio sources, known collectively as the 21-cm forest, have been demonstrated as a sensitive probe to the small-scale structures governed by the dark matter (DM) properties, as well as the thermal history of the intergalactic medium regulated by the first galaxies during the epoch of reionization. By statistically analyzing these spectral features, the one-dimensional (1D) power spectrum of the 21-cm forest can effectively break the parameter degeneracies and constrain the properties of both DM and the first galaxies. However, conventional parameter inference methods face challenges due to computationally expensive simulations for 21-cm forest and the non-Gaussian signal characteristics. To address these issues, we introduce generative normalizing flows for data augmentation and inference normalizing flows for parameters estimation. This approach efficiently estimates parameters from minimally simulated datasets with non-Gaussian signals. Using simulated data from the upcoming Square Kilometre Array (SKA), we demonstrate the ability of the deep learning-driven likelihood-free approach to generate accurate posterior distributions, providing a robust and efficient tool for probing DM and the cosmic heating history using the 1D power spectrum of 21-cm forest in the era of SKA. This methodology is adaptable for scientific analyses with other unevenly distributed data.

astro-ph.CO↗

arXiv2025

Model-independent $H_0$ within FLRW: Joint constraints from GWTC-3 standard sirens and strong lensing time delays

We use 47 gravitational-wave (GW) standard sirens from the third Gravitational-Wave Transient Catalog to calibrate distances in the strong gravitational lensing (SGL) system RXJ1131-1231 and constrain the Hubble constant ($H_0$) via the distance sum rule, without assuming a specific cosmological model. For $Ω_K = 0$, we obtain $H_0 = 73.22^{+5.95}_{-5.43}$ ${\rm km}~{\rm s}^{-1}~{\rm Mpc}^{-1}$ and $H_0 = 70.40^{+8.03}_{-5.60}$ ${\rm km}~{\rm s}^{-1}~{\rm Mpc}^{-1}$ by breaking the mass-sheet transform using lens galaxy's mass models and stellar kinematics, respectively. Allowing $Ω_K$ to vary increases the central value of $H_0$ and reduces its precision. We find that GW dark sirens have significant potential for calibrating SGL systems, due to their relatively higher redshifts. By combining 42 binary black holes and RXJ1131-1231, we obtain an $H_0$ constraint with a precision approximately $40\%$ better than the measurement from GW170817 using the Hubble law. This suggests that high-precision, model-independent $H_0$ measurements can be achieved with this method as the redshift range of GW dark sirens expands, even without the need for GW bright sirens.

astro-ph.CO↗

arXiv2025

EAVIT: Efficient and Accurate Human Value Identification from Text data via LLMs

The rapid evolution of large language models (LLMs) has revolutionized various fields, including the identification and discovery of human values within text data. While traditional NLP models, such as BERT, have been employed for this task, their ability to represent textual data is significantly outperformed by emerging LLMs like GPTs. However, the performance of online LLMs often degrades when handling long contexts required for value identification, which also incurs substantial computational costs. To address these challenges, we propose EAVIT, an efficient and accurate framework for human value identification that combines the strengths of both locally fine-tunable and online black-box LLMs. Our framework employs a value detector - a small, local language model - to generate initial value estimations. These estimations are then used to construct concise input prompts for online LLMs, enabling accurate final value identification. To train the value detector, we introduce explanation-based training and data generation techniques specifically tailored for value identification, alongside sampling strategies to optimize the brevity of LLM input prompts. Our approach effectively reduces the number of input tokens by up to 1/6 compared to directly querying online LLMs, while consistently outperforming traditional NLP methods and other LLM-based strategies.

cs.CL↗

arXiv2025

Bethe-ansatz diagonalization of steady state of boundary driven integrable spin chains

We find that the density operator of non-equilibrium steady state (NESS) of XXZ spin chain with strong ``sink and source" boundary dissipation, can be described in terms of quasiparticles, with renormalized -- dissipatively dressed -- dispersion relation. The spectrum of the NESS is then fully accounted for by Bethe ansatz equations for an associated coherent system. The dissipative dressing generates an extra singularity in the dispersion relation, which strongly modifies the NESS spectrum with respect to the spectrum of the corresponding coherent model. In particular, this leads to a dissipation-assisted entropy reduction, due to the suppression -- in the NESS spectrum -- of plain wave-type Bethe states in favor of Bethe states localized at the boundaries.

cond-mat.stat-mech↗

arXiv2025

Beyond Modality Collapse: Representations Blending for Multimodal Dataset Distillation

Multimodal Dataset Distillation (MDD) seeks to condense large-scale image-text datasets into compact surrogates while retaining their effectiveness for cross-modal learning. Despite recent progress, existing MDD approaches often suffer from \textit{\textbf{Modality Collapse}}, characterized by over-concentrated intra-modal representations and enlarged distributional gap across modalities. In this paper, at the first time, we identify this issue as stemming from a fundamental conflict between the over-compression behavior inherent in dataset distillation and the cross-modal supervision imposed by contrastive objectives. To alleviate modality collapse, we introduce \textbf{RepBlend}, a novel MDD framework that weakens overdominant cross-modal supervision via representation blending, thereby significantly enhancing intra-modal diversity. Additionally, we observe that current MDD methods impose asymmetric supervision across modalities, resulting in biased optimization. To address this, we propose symmetric projection trajectory matching, which synchronizes the optimization dynamics using modality-specific projection heads, thereby promoting balanced supervision and enhancing cross-modal alignment. Experiments on Flickr-30K and MS-COCO show that RepBlend consistently outperforms prior state-of-the-art MDD methods, achieving significant gains in retrieval performance (e.g., +9.4 IR@10, +6.3 TR@10 under the 100-pair setting) and offering up to 6.7$\times$ distillation speedup.

cs.CV↗

arXiv2025

Gaussian Shading++: Rethinking the Realistic Deployment Challenge of Performance-Lossless Image Watermark for Diffusion Models

Ethical concerns surrounding copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models. One effective solution involves watermarking the generated images. Existing methods primarily focus on ensuring that watermark embedding does not degrade the model performance. However, they often overlook critical challenges in real-world deployment scenarios, such as the complexity of watermark key management, user-defined generation parameters, and the difficulty of verification by arbitrary third parties. To address this issue, we propose Gaussian Shading++, a diffusion model watermarking method tailored for real-world deployment. We propose a double-channel design that leverages pseudorandom error-correcting codes to encode the random seed required for watermark pseudorandomization, achieving performance-lossless watermarking under a fixed watermark key and overcoming key management challenges. Additionally, we model the distortions introduced during generation and inversion as an additive white Gaussian noise channel and employ a novel soft decision decoding strategy during extraction, ensuring strong robustness even when generation parameters vary. To enable third-party verification, we incorporate public key signatures, which provide a certain level of resistance against forgery attacks even when model inversion capabilities are fully disclosed. Extensive experiments demonstrate that Gaussian Shading++ not only maintains performance losslessness but also outperforms existing methods in terms of robustness, making it a more practical solution for real-world deployment.

cs.CV↗

arXiv2025

Revisiting holographic dark energy after DESI 2024

New insights from the Dark Energy Spectroscopic Instrument (DESI) 2024 baryon acoustic oscillations (BAO) data, in conjunction with cosmic microwave background (CMB) and Type Ia supernova (SN) data, suggest that dark energy may not be a cosmological constant. In this work, we investigate the cosmological implications of holographic dark energy (HDE) and interacting holographic dark energy (IHDE) models, utilizing CMB, DESI BAO, and SN data. By considering the combined DESI BAO and SN data, we determine that in the IHDE model, the parameter $c > 1$ and the dark-energy equation of state $w$ does not cross $-1$ at the $1σ$ confidence level, whereas in the HDE model, it marginally falls below this threshold. Upon incorporating CMB data, we observe that in the HDE model, the parameter $c < 1$ and $w$ crosses $-1$ at a level beyond $10σ$. Conversely, for the IHDE model, the likelihood of $w$ crossing $-1$ is considerably diminished, implying that the introduction of interaction within the HDE model could potentially resolve or mitigate the cosmic big rip conundrum. Furthermore, our analysis reveals that the HDE and IHDE models are statistically as viable as the $Λ$CDM model when assessing Bayesian evidence with DESI BAO data combined with SN data. However, when CMB data are added, the HDE and IHDE models are significantly less favored compared to the $Λ$CDM model. Our findings advocate for further exploration of the HDE and IHDE models using forthcoming, more precise late-universe observations.

astro-ph.CO↗

arXiv2025

Evaluating the Accuracy of Non-parametric Galaxy Morphological Indicator Measurements in the CSST Imaging Survey

The Chinese Space Station Telescope (CSST) is China's upcoming next-generation ultraviolet and optical survey telescope, with imaging resolution capabilities comparable to the Hubble Space Telescope (HST). In this study, we utilized a comprehensive sample of 3,679 CSST realistic mock galaxies constructed from HST CANDELS/GOODS-North deep imaging observations, with stellar masses $\log\left(M_{*} / M_{\odot}\right) > 9.0$ and redshifts $z < 2$. We evaluate the detection capabilities of CSST surveys and the accuracy in measuring the non-parametric morphological indicators ($C$, $A$, $Gini$, $M_{\rm 20}$, $A_{\rm O}$, $D_{\rm O}$) of galaxies. Our findings show that in terms of galaxy detection capabilities, CSST's deep field surveys can achieve the same level as HST's deep field observations; however, in wide-field surveys, CSST exhibits a significant deficiency in detecting high-redshift, low-mass, low-surface-brightness galaxies. Regarding the measurement of galaxy morphology, CSST's deep field surveys achieve high accuracy across all indicators except for the asymmetry indicator ($A$), whereas its wide-field surveys suffer from significant systematic biases. We thus provide simple correction functions to adjust the non-parametric morphological indicators obtained from CSST's wide-field and deep-field observations, thereby aligning CSST measurements with those from HST. This adjustment enables the direct application of non-parametric morphological classification methods originally developed for HST data to galaxies observed by CSST.

astro-ph.GA↗

arXiv2025

Nonlinearization of the KdV-type and mKdV-type bilinear equations

In this paper, we show a general procedure to nonlinearize bilinear equations by using the Bell polynomials. As applications, we obtain nonlinear forms of some integrable bilinear equations (in the sense having 3-soliton solutions) of the KdV type and mKdV type that were found by Jarmo Hietarinta in 1980s. Examples of non-integrable bilinear equations of the KdV type are also given.

nlin.SI↗

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

A Survey on 3D Reconstruction Techniques in Plant Phenotyping: From Classical Methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and Beyond

Plant phenotyping plays a pivotal role in understanding plant traits and their interactions with the environment, making it crucial for advancing precision agriculture and crop improvement. 3D reconstruction technologies have emerged as powerful tools for capturing detailed plant morphology and structure, offering significant potential for accurate and automated phenotyping. This paper provides a comprehensive review of the 3D reconstruction techniques for plant phenotyping, covering classical reconstruction methods, emerging Neural Radiance Fields (NeRF), and the novel 3D Gaussian Splatting (3DGS) approach. Classical methods, which often rely on high-resolution sensors, are widely adopted due to their simplicity and flexibility in representing plant structures. However, they face challenges such as data density, noise, and scalability. NeRF, a recent advancement, enables high-quality, photorealistic 3D reconstructions from sparse viewpoints, but its computational cost and applicability in outdoor environments remain areas of active research. The emerging 3DGS technique introduces a new paradigm in reconstructing plant structures by representing geometry through Gaussian primitives, offering potential benefits in both efficiency and scalability. We review the methodologies, applications, and performance of these approaches in plant phenotyping and discuss their respective strengths, limitations, and future prospects (https://github.com/JiajiaLi04/3D-Reconstruction-Plants). Through this review, we aim to provide insights into how these diverse 3D reconstruction techniques can be effectively leveraged for automated and high-throughput plant phenotyping, contributing to the next generation of agricultural technology.

eess.IV↗

arXiv2025

Explicit Motion Handling and Interactive Prompting for Video Camouflaged Object Detection

Camouflage poses challenges in distinguishing a static target, whereas any movement of the target can break this disguise. Existing video camouflaged object detection (VCOD) approaches take noisy motion estimation as input or model motion implicitly, restricting detection performance in complex dynamic scenes. In this paper, we propose a novel Explicit Motion handling and Interactive Prompting framework for VCOD, dubbed EMIP, which handles motion cues explicitly using a frozen pre-trained optical flow fundamental model. EMIP is characterized by a two-stream architecture for simultaneously conducting camouflaged segmentation and optical flow estimation. Interactions across the dual streams are realized in an interactive prompting way that is inspired by emerging visual prompt learning. Two learnable modules, i.e., the camouflaged feeder and motion collector, are designed to incorporate segmentation-to-motion and motion-to-segmentation prompts, respectively, and enhance outputs of the both streams. The prompt fed to the motion stream is learned by supervising optical flow in a self-supervised manner. Furthermore, we show that long-term historical information can also be incorporated as a prompt into EMIP and achieve more robust results with temporal consistency. Experimental results demonstrate that our EMIP achieves new state-of-the-art records on popular VCOD benchmarks. Our code is made publicly available at https://github.com/zhangxin06/EMIP.

cs.CV↗

arXiv2025

Provably Secure Public-Key Steganography Based on Admissible Encoding

The technique of hiding secret messages within seemingly harmless covertext to evade examination by censors with rigorous security proofs is known as provably secure steganography (PSS). PSS evolves from symmetric key steganography to public-key steganography, functioning without the requirement of a pre-shared key and enabling the extension to multi-party covert communication and identity verification mechanisms. Recently, a public-key steganography method based on elliptic curves was proposed, which uses point compression to eliminate the algebraic structure of curve points. However, this method has strict requirements on the curve parameters and is only available on half of the points. To overcome these limitations, this paper proposes a more general elliptic curve public key steganography method based on admissible encoding. By applying the tensor square function to the known well-distributed encoding, we construct admissible encoding, which can create the pseudo-random public-key encryption function. The theoretical analysis and experimental results show that the proposed provable secure public-key steganography method can be deployed on all types of curves and utilize all points on the curve.

cs.CR↗

arXiv2025

TabulaTime: A Novel Multimodal Deep Learning Framework for Advancing Acute Coronary Syndrome Prediction through Environmental and Clinical Data Integration

Acute Coronary Syndromes (ACS), including ST-segment elevation myocardial infarctions (STEMI) and non-ST-segment elevation myocardial infarctions (NSTEMI), remain a leading cause of mortality worldwide. Traditional cardiovascular risk scores rely primarily on clinical data, often overlooking environmental influences like air pollution that significantly impact heart health. Moreover, integrating complex time-series environmental data with clinical records is challenging. We introduce TabulaTime, a multimodal deep learning framework that enhances ACS risk prediction by combining clinical risk factors with air pollution data. TabulaTime features three key innovations: First, it integrates time-series air pollution data with clinical tabular data to improve prediction accuracy. Second, its PatchRWKV module automatically extracts complex temporal patterns, overcoming limitations of traditional feature engineering while maintaining linear computational complexity. Third, attention mechanisms enhance interpretability by revealing interactions between clinical and environmental factors. Experimental results show that TabulaTime improves prediction accuracy by over 20% compared to conventional models such as CatBoost, Random Forest, and LightGBM, with air pollution data alone contributing over a 10% improvement. Feature importance analysis identifies critical predictors including previous angina, systolic blood pressure, PM10, and NO2. Overall, TabulaTime bridges clinical and environmental insights, supporting personalized prevention strategies and informing public health policies to mitigate ACS risk.

cs.AI↗

arXiv2025

An Accelerated Camera 3DMA Framework for Efficient Urban GNSS Multipath Estimation

Robust GNSS positioning in urban environments is still plagued by multipath effects, particularly due to the complex signal propagation induced by ubiquitous surfaces with varied radio frequency reflectivities. Current 3D Mapping Aided (3DMA) GNSS techniques show great potentials in mitigating multipath but face a critical trade-off between computational efficiency and modeling accuracy. Most approaches often rely on offline outdated or oversimplified 3D maps, while real-time LiDAR-based reconstruction boasts high accuracy, it is problematic in low laser reflectivity conditions; camera 3DMA is a good candidate to balance accuracy and efficiency but current methods suffer from extremely low reconstruction speed, a far cry from real-time multipath-mitigated navigation. This paper proposes an accelerated framework incorporating camera multi-view stereo (MVS) reconstruction and ray tracing. By hypothesizing on surface textures, an orthogonal visual feature fusion framework is proposed, which robustly addresses both texture-rich and texture-poor surfaces, lifting off the reflectivity challenges in visual reconstruction. A polygonal surface modeling scheme is further integrated to accurately delineate complex building boundaries, enhancing the reconstruction granularity. To avoid excessively accurate reconstruction, reprojected point cloud multi-plane fitting and two complexity control strategies are proposed, thus improving upon multipath estimation speed. Experiments were conducted in Lujiazui, Shanghai, a typical multipath-prone district. The results show that the method achieves an average reconstruction accuracy of 2.4 meters in dense urban environments featuring glass curtain wall structures, a traditionally tough case for reconstruction, and achieves a ray-tracing-based multipath correction rate of 30 image frames per second, 10 times faster than the contemporary benchmarks.

eess.SP↗