arXiv subjects

Xin Zhang

Publications and source records attributed to Xin Zhang.

At least 469 records · Page 26Linked to original sources

arXiv2025

Ultra-low-frequency gravitational waves from individual supermassive black hole binaries as standard sirens

Ultra-low-frequency gravitational waves (GWs) generated by individual inspiraling supermassive black hole binaries (SMBHBs) at the centers of galaxies may be detected by pulsar timing arrays (PTAs) in the future. These GW signals, which encode absolute cosmic distances, can serve as bright and dark sirens, potentially evolving into a precise cosmological probe. Here, we show that a PTA in the era of the Square Kilometre Array, comprising 100 millisecond pulsars, could potentially detect about 25 bright sirens and 41 dark sirens over a 10-year observation period. The bright sirens, combined with cosmic microwave background data, offer capabilities comparable to current mainstream joint cosmological observations for measuring the equation of state of dark energy. The dark sirens could achieve a measurement precision of the Hubble constant close to that of current distance-ladder observations. Our results suggest that ultra-low-frequency GWs from individual SMBHBs are of great significance in investigating the nature of dark energy and determining the Hubble constant.

astro-ph.CO↗

arXiv2025

Deep k-grouping: An Unsupervised Learning Framework for Combinatorial Optimization on Graphs and Hypergraphs

Along with AI computing shining in scientific discovery, its potential in the combinatorial optimization (CO) domain has also emerged in recent years. Yet, existing unsupervised neural network solvers struggle to solve $k$-grouping problems (e.g., coloring, partitioning) on large-scale graphs and hypergraphs, due to limited computational frameworks. In this work, we propose Deep $k$-grouping, an unsupervised learning-based CO framework. Specifically, we contribute: Novel one-hot encoded polynomial unconstrained binary optimization (OH-PUBO), a formulation for modeling k-grouping problems on graphs and hypergraphs (e.g., graph/hypergraph coloring and partitioning); GPU-accelerated algorithms for large-scale k-grouping CO problems. Deep $k$-grouping employs the relaxation of large-scale OH-PUBO objectives as differentiable loss functions and trains to optimize them in an unsupervised manner. To ensure scalability, it leverages GPU-accelerated algorithms to unify the training pipeline; A Gini coefficient-based continuous relaxation annealing strategy to enforce discreteness of solutions while preventing convergence to local optima. Experimental results demonstrate that Deep $k$-grouping outperforms existing neural network solvers and classical heuristics such as SCIP and Tabu.

cs.LG↗

arXiv2025

BIPNN: Learning to Solve Binary Integer Programming via Hypergraph Neural Networks

Binary (0-1) integer programming (BIP) is pivotal in scientific domains requiring discrete decision-making. As the advance of AI computing, recent works explore neural network-based solvers for integer linear programming (ILP) problems. Yet, they lack scalability for tackling nonlinear challenges. To handle nonlinearities, state-of-the-art Branch-and-Cut solvers employ linear relaxations, leading to exponential growth in auxiliary variables and severe computation limitations. To overcome these limitations, we propose BIPNN (Binary Integer Programming Neural Network), an unsupervised learning framework to solve nonlinear BIP problems via hypergraph neural networks (HyperGNN). Specifically, BIPNN reformulates BIPs-constrained, discrete, and nonlinear (sin, log, exp) optimization problems-into unconstrained, differentiable, and polynomial loss functions. The reformulation stems from the observation of a precise one-to-one mapping between polynomial BIP objectives and hypergraph structures, enabling the unsupervised training of HyperGNN to optimize BIP problems in an end-to-end manner. On this basis, we propose a GPU-accelerated and continuous-annealing-enhanced training pipeline for BIPNN. The pipeline enables BIPNN to optimize large-scale nonlinear terms in BIPs fully in parallel via straightforward gradient descent, thus significantly reducing the training cost while ensuring the generation of discrete, high-quality solutions. Extensive experiments on synthetic and real-world datasets highlight the superiority of our approach.

cs.LG↗

arXiv2025

Language Models are Universal Embedders

In the large language model (LLM) revolution, embedding is a key component of various systems, such as retrieving knowledge or memories for LLMs or building content moderation filters. As such cases span from English to other natural or programming languages, from retrieval to classification and beyond, it is advantageous to build a unified embedding model rather than dedicated ones for each scenario. In this context, the pre-trained multilingual decoder-only large language models, e.g., BLOOM, emerge as a viable backbone option. To assess their potential, we propose straightforward strategies for constructing embedders and introduce a universal evaluation benchmark. Experimental results show that our trained model is proficient at generating good embeddings across languages and tasks, even extending to languages and tasks for which no finetuning/pretraining data is available. We also present detailed analyses and additional evaluations. We hope that this work could encourage the development of more robust open-source universal embedders.

cs.CL↗

arXiv2025

UrbanMind: Urban Dynamics Prediction with Multifaceted Spatial-Temporal Large Language Models

Understanding and predicting urban dynamics is crucial for managing transportation systems, optimizing urban planning, and enhancing public services. While neural network-based approaches have achieved success, they often rely on task-specific architectures and large volumes of data, limiting their ability to generalize across diverse urban scenarios. Meanwhile, Large Language Models (LLMs) offer strong reasoning and generalization capabilities, yet their application to spatial-temporal urban dynamics remains underexplored. Existing LLM-based methods struggle to effectively integrate multifaceted spatial-temporal data and fail to address distributional shifts between training and testing data, limiting their predictive reliability in real-world applications. To bridge this gap, we propose UrbanMind, a novel spatial-temporal LLM framework for multifaceted urban dynamics prediction that ensures both accurate forecasting and robust generalization. At its core, UrbanMind introduces Muffin-MAE, a multifaceted fusion masked autoencoder with specialized masking strategies that capture intricate spatial-temporal dependencies and intercorrelations among multifaceted urban dynamics. Additionally, we design a semantic-aware prompting and fine-tuning strategy that encodes spatial-temporal contextual details into prompts, enhancing LLMs' ability to reason over spatial-temporal patterns. To further improve generalization, we introduce a test time adaptation mechanism with a test data reconstructor, enabling UrbanMind to dynamically adjust to unseen test data by reconstructing LLM-generated embeddings. Extensive experiments on real-world urban datasets across multiple cities demonstrate that UrbanMind consistently outperforms state-of-the-art baselines, achieving high accuracy and robust generalization, even in zero-shot settings.

cs.LG↗

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↗