arXiv subjects

Tao Wang

Publications and source records attributed to Tao Wang.

At least 235 records · Page 13Linked to original sources

arXiv2025

An all-sky 3D dust map Based on Gaia and LAMOST

We present a comprehensive 3D dust reddening map covering the entire Milky Way, constructed by combining reddening estimates based on LAMOST low-resolution spectra (E(B$-$V)$_{\rm LAMOST}$) with those derived from $Gaia$ XP spectra (E(B$-$V)$_{\rm XP}$), along with revised $Gaia$ distances. E(B$-$V)$_{\rm LAMOST}$ values of $\sim$ 4.6 million unique sources were obtained with the standard-pair analysis using LAMOST DR11 stellar parameters and synthesized $B/V$-band photometry from $Gaia$ XP spectra, showing a typical precision of $\sim$ 0.01 mag. The E(B$-$V)$_{\rm XP}$ from the catalog of \citet{zhang2023}, which was derived using forward modeling of $Gaia$ XP spectra, were cross-validated with E(B$-$V)$_{\rm LAMOST}$, leading to the selection of $\sim$ 150 million high-reliability measurements. The combined dataset achieves a median precision of $\sim$ 0.03 mag for E(B$-$V). To model the reddening -- distance relationship along various lines-of-sight, we implemented a parametric approach that accounts for contributions from the local bubble, diffuse interstellar-medium, and multiple potential molecular clouds. The sky was adaptively partitioned based on stellar density, resulting in angular resolutions ranging from 3.4$^{\prime}$ to 58$^{\prime}$, with about half of the sky having a resolution better than 6.9$^{\prime}$. The reddening precision of our 3D map for individual stars reaches $\sim$ 0.01 mag in most regions at $|b| > 20^\circ$, but degrades to 0.01-0.05 mag at $|b| < 20^\circ$. The map reaches a maximum distance of 3-5 kpc in high-extinction regions with $|b| < 5^\circ$, and extends to 10-15 kpc elsewhere. An interactive platform and Python package have been developed for utilization of the 3D dust map. Available online: https://nadc.china-vo.org/data/dustmaps/.

astro-ph.GA↗

arXiv2025

A Comprehensive All-Sky Catalog of 3345 Molecular Clouds from Three-dimensional Dust Extinction

Understanding the distribution and properties of molecular clouds is crucial for tracing the structure and evolution of the interstellar medium and the large-scale morphology of the Milky Way. Here we present an all-sky catalog of 3,345 molecular clouds identified from our previous three-dimensional dust reddening map using a dendrogram-based clustering method with distance-adaptive parameters. The catalog spans heliocentric distances from 90 pc to 4.3 kpc and includes key physical properties for each cloud, including position, size, mass, surface density, and dust density. Approximately 650 clouds in our catalog are associated with the boundary of the Local Bubble, while around 740 clouds (excluding those associated with the Local Bubble) are located at high Galactic latitudes ($|b| > 20^\circ$). The spatial distribution of the cataloged clouds reveals prominent large-scale features in the Galactic disk, including coherent spur-like structures, large-scale cavities, and a more detailed view of the Local Bubble shell. These findings refine our understanding of how molecular clouds trace the Galactic spiral arm network and provide new insight into the spatial structure of the Local Bubble. The catalog serves as a valuable resource for future studies of star formation, Galactic structure, and the interaction between molecular clouds and large-scale ISM features.

astro-ph.GA↗

arXiv2025

Investigating Little Red Dots with UV Excess: Are They the High-Redshift Siblings of Blue Hot DOGs?

Little Red Dots (LRDs), newly identified compact and dusty galaxies with an unexpectedly high number density observed by JWST, have an unusual "V-shaped" rest-frame UV to near-infrared spectral energy distribution (SED). A group of hyper-luminous, obscured quasars with excess blue emission, called Blue-excess Hot Dust-Obscured Galaxies (BHDs), also exhibit qualitatively similar SEDs to those of LRDs. They represent a rare population of galaxies hosting supermassive black holes (SMBHs) accreting near the Eddington limit at redshifts z \sim 1--4. In this study, we compare their multi-wavelength SEDs to investigate whether LRDs, or a subset of them, could be high-redshift analogs of BHDs. Our analysis reveals that despite their similar "V-shape" SEDs, LRDs appear to be a different population than BHDs. The "V-shape" of BHDs appear at longer wavelengths compared to LRDs due to different selection strategies, suggesting LRDs have much less dust attenuation than typical BHDs. The bluer colors in the rest-frame infrared (continuum) emission of LRDs suggest the absence of hot dust heated by AGN accretion activities. We also argue that the blue excess in LRDs is unlikely from AGN scattered light. The compact morphologies and lower X-ray detection frequencies of LRDs suggest a distinct formation pathway from BHDs -- which are thought to be powered by super-Eddington accretion onto central SMBHs following major galaxy mergers.

astro-ph.GA↗

arXiv2025

PromptRR: Diffusion Models as Prompt Generators for Single Image Reflection Removal

Existing single image reflection removal (SIRR) methods using deep learning tend to miss key low-frequency (LF) and high-frequency (HF) differences in images, affecting their effectiveness in removing reflections. To address this problem, this paper proposes a novel prompt-guided reflection removal (PromptRR) framework that uses frequency information as new visual prompts for better reflection performance. Specifically, the proposed framework decouples the reflection removal process into the prompt generation and subsequent prompt-guided restoration. For the prompt generation, we first propose a prompt pre-training strategy to train a frequency prompt encoder that encodes the ground-truth image into LF and HF prompts. Then, we adopt diffusion models (DMs) as prompt generators to generate the LF and HF prompts estimated by the pre-trained frequency prompt encoder. For the prompt-guided restoration, we integrate specially generated prompts into the PromptFormer network, employing a novel Transformer-based prompt block to effectively steer the model toward enhanced reflection removal. The results on commonly used benchmarks show that our method outperforms state-of-the-art approaches. The codes and models are available at https://github.com/TaoWangzj/PromptRR.

cs.CV↗

arXiv2025

The Milky Way is a less massive galaxy--new estimates of the Milky Way's local and global stellar masses

Stellar mass is the most fundamental property of a galaxy. While it has been robustly measured for millions of external galaxies, it remains poorly constrained for the Milky Way because of the strong selection effect from our inside perspective. In this work, we reconstruct the intrinsic vertical mass density profile in the solar neighborhood and the radial mass density profile across the entire Galaxy using data from the Gaia and APOGEE surveys, following careful correction for the selection function. The local density profile exhibits strong north-south asymmetry in the geometric thick disk regime and increases steeply toward the disk mid-plane, favoring an exponential model over the sech$^2$ model. Integrating the local vertical density profile yields a surface stellar mass density of 31.563$\pm$2.813(syst.)$\pm$0.024(stoch.)~${\rm M_{\odot}pc^{-2}}$, of which 25.074 and 6.489~${\rm M_{\odot}pc^{-2}}$ correspond to living stars and stellar remnants, respectively. The radial surface mass density profile {of the Milky Way} shares the same flat inner component as observed in the brightness profile. With this mass density profile {and local mass density from Gaia}, we derive a new estimate of the total stellar mass of the Milky Way of 2.607$\pm$0.353(syst.)$\pm$0.085(stoch.)${\rm \times10^{10}M_{\odot}}$, a factor of two lower than the previous results. This discrepancy arises primarily from the inner disk profile, which was previously unavailable and extrapolated from the outer disk profile. The lower stellar mass estimate of the Milky Way significantly reduces its rarity in terms of supermassive black hole mass among external galaxies and implies a larger dark matter-to-baryon mass ratio in the inner Galaxy.

astro-ph.GA↗

arXiv2025

P/D-Device: Disaggregated Large Language Model between Cloud and Devices

Serving disaggregated large language models has been widely adopted in industrial practice for enhanced performance. However, too many tokens generated in decoding phase, i.e., occupying the resources for a long time, essentially hamper the cloud from achieving a higher throughput. Meanwhile, due to limited on-device resources, the time to first token (TTFT), i.e., the latency of prefill phase, increases dramatically with the growth on prompt length. In order to concur with such a bottleneck on resources, i.e., long occupation in cloud and limited on-device computing capacity, we propose to separate large language model between cloud and devices. That is, the cloud helps a portion of the content for each device, only in its prefill phase. Specifically, after receiving the first token from the cloud, decoupling with its own prefill, the device responds to the user immediately for a lower TTFT. Then, the following tokens from cloud are presented via a speed controller for smoothed TPOT (the time per output token), until the device catches up with the progress. On-device prefill is then amortized using received tokens while the resource usage in cloud is controlled. Moreover, during cloud prefill, the prompt can be refined, using those intermediate data already generated, to further speed up on-device inference. We implement such a scheme P/D-Device, and confirm its superiority over other alternatives. We further propose an algorithm to decide the best settings. Real-trace experiments show that TTFT decreases at least 60%, maximum TPOT is about tens of milliseconds, and cloud throughput increases by up to 15x.

cs.DC↗

arXiv2025

Arbitrarily-high-dimensional reconciliation via cross-rotation for continuous-variable quantum key distribution

Multidimensional rotation serves as a powerful tool for enhancing information reconciliation and extending the transmission distance in continuous-variable quantum key distribution (CV-QKD). However, the lack of closed-form orthogonal transformations for high-dimensional rotations has limited the maximum reconciliation efficiency to channels with 8 dimensions over the past decade. This paper presents a cross-rotation scheme to overcome this limitation and enable reconciliation in arbitrarily high dimensions, constrained to even multiples of 8. The key treatment involves reshaping the string vector into matrix form and applying orthogonal transformations to its columns and rows in a cross manner, thereby increasing the reconciliation dimension by one order per cross-rotation while significantly reducing the communication overhead over the classical channel. A rigorous performance analysis is also presented from the perspective of achievable sum-rate. Simulation results demonstrate that 64-dimensional cross-rotation nearly approaches the upper bound, making it a recommended choice for practical implementations.

quant-ph↗

arXiv2025

Dual Prompt Learning for Adapting Vision-Language Models to Downstream Image-Text Retrieval

Recently, prompt learning has demonstrated remarkable success in adapting pre-trained Vision-Language Models (VLMs) to various downstream tasks such as image classification. However, its application to the downstream Image-Text Retrieval (ITR) task is more challenging. We find that the challenge lies in discriminating both fine-grained attributes and similar subcategories of the downstream data. To address this challenge, we propose Dual prompt Learning with Joint Category-Attribute Reweighting (DCAR), a novel dual-prompt learning framework to achieve precise image-text matching. The framework dynamically adjusts prompt vectors from both semantic and visual dimensions to improve the performance of CLIP on the downstream ITR task. Based on the prompt paradigm, DCAR jointly optimizes attribute and class features to enhance fine-grained representation learning. Specifically, (1) at the attribute level, it dynamically updates the weights of attribute descriptions based on text-image mutual information correlation; (2) at the category level, it introduces negative samples from multiple perspectives with category-matching weighting to learn subcategory distinctions. To validate our method, we construct the Fine-class Described Retrieval Dataset (FDRD), which serves as a challenging benchmark for ITR in downstream data domains. It covers over 1,500 downstream fine categories and 230,000 image-caption pairs with detailed attribute annotations. Extensive experiments on FDRD demonstrate that DCAR achieves state-of-the-art performance over existing baselines.

cs.CV↗

arXiv2025

Variational Bernstein-von Mises theorem with increasing parameter dimension

Variational Bayes (VB) provides a computationally efficient alternative to Markov Chain Monte Carlo, especially for high-dimensional and large-scale inference. However, existing theory on VB primarily focuses on fixed-dimensional settings or specific models. To address this limitation, this paper develops a finite-sample theory for VB in a broad class of parametric models with latent variables. We establish theoretical properties of the VB posterior, including a non-asymptotic variational Bernstein--von Mises theorem. Furthermore, we derive consistency and asymptotic normality of the VB estimator. An application to multivariate Gaussian mixture models is presented for illustration.

math.ST↗

arXiv2025

SecoustiCodec: Cross-Modal Aligned Streaming Single-Codecbook Speech Codec

Speech codecs serve as a crucial bridge in unifying speech and text language models. Existing codec methods face several challenges in semantic encoding, such as residual paralinguistic information (e.g., timbre, emotion), insufficient semantic completeness, limited reconstruction capability, and lack of support for streaming. To address these challenges, we propose SecoustiCodec, a cross-modal aligned low-bitrate streaming speech codec that disentangles semantic and paralinguistic information in a single-codebook space. To ensure semantic completeness and reconstruction fidelity, paralinguistic encoding is introduced to bridge the information gap between semantic and acoustic encoding. A semantic-only efficient quantization method based on VAE (Variational Autoencoder) and FSQ (Finite Scalar Quantization) is proposed. This approach alleviates the long-tail distribution problem of tokens while maintaining high codebook utilization. A semantic disentanglement method based on contrastive learning is proposed, which aligns text and speech in a joint multimodal frame-level space, effectively removing paralinguistic information from semantic encoding. An acoustic-constrained multi-stage optimization strategy is proposed to ensure robust and stable convergence. Figure~\ref{fig:pesq_kbps_below_2kbps} shows SecoustiCodec achieves SOTA (state-of-the-art) reconstruction quality (PESQ) of 1.77/2.58 at 0.27/1 kbps. The code and model weights for SecoustiCodec will be open-sourced upon the completion of the peer-review process. We've open-sourced SecoustiCodec's demo, code, and model weights.

eess.AS↗

arXiv2025

Data-driven RF Tomography via Cross-modal Sensing and Continual Learning

Data-driven radio frequency (RF) tomography has demonstrated significant potential for underground target detection, due to the penetrative nature of RF signals through soil. However, it is still challenging to achieve accurate and robust performance in dynamic environments. In this work, we propose a data-driven radio frequency tomography (DRIFT) framework with the following key components to reconstruct cross section images of underground root tubers, even with significant changes in RF signals. First, we design a cross-modal sensing system with RF and visual sensors, and propose to train an RF tomography deep neural network (DNN) model following the cross-modal learning approach. Then we propose to apply continual learning to automatically update the DNN model, once environment changes are detected in a dynamic environment. Experimental results show that our approach achieves an average equivalent diameter error of 2.29 cm, 23.2% improvement upon the state-of-the-art approach. Our DRIFT code and dataset are publicly available on https://github.com/Data-driven-RTI/DRIFT.

eess.SP↗

arXiv2025

Glass Surface Segmentation with an RGB-D Camera via Weighted Feature Fusion for Service Robots

We address the problem of glass surface segmentation with an RGB-D camera, with a focus on effectively fusing RGB and depth information. To this end, we propose a Weighted Feature Fusion (WFF) module that dynamically and adaptively combines RGB and depth features to tackle issues such as transparency, reflections, and occlusions. This module can be seamlessly integrated with various deep neural network backbones as a plug-and-play solution. Additionally, we introduce the MJU-Glass dataset, a comprehensive RGB-D dataset collected by a service robot navigating real-world environments, providing a valuable benchmark for evaluating segmentation models. Experimental results show significant improvements in segmentation accuracy and robustness, with the WFF module enhancing performance in both mean Intersection over Union (mIoU) and boundary IoU (bIoU), achieving a 7.49% improvement in bIoU when integrated with PSPNet. The proposed module and dataset provide a robust framework for advancing glass surface segmentation in robotics and reducing the risk of collisions with glass objects.

cs.CV↗

arXiv2025

Noema formIng Cluster survEy (NICE): A Census of Star Formation and Cold Gas Properties in Massive protoclusters at 1.5<z<4

Massive protoclusters at z~1.5-4, the peak of the cosmic star formation history, are key to understanding the formation mechanisms of massive galaxies in today's clusters. However, studies of protoclusters at these high redshifts remain limited, primarily due to small sample sizes and heterogeneous selection criteria. In this work, we conduct a systematic investigation of the star formation and cold gas properties of member galaxies of eight massive protoclusters in the COSMOS field, using the statistical and homogeneously selected sample from the Noema formIng Cluster survEy (NICE). Our analysis reveals a steep increase in the star formation rates per halo mass ($Σ_{\rm SFR} /M_{\rm halo}$) with redshifts in these intensively star-forming protoclusters, reaching values one to two orders of magnitude higher than those observed in the field at z>2. We further show that, instead of an enhancement of starbursts, this increase is largely driven by the concentration of massive and gas-rich star-forming galaxies in the protocluster cores. The member galaxies still generally follow the same star formation main sequence as in the field, with a moderate enhancement at the low mass end. Notably, the most massive protocluster galaxies ($M_\star$>8$\times$10$^{10}$M$_\odot$) exhibit higher $f_{\rm gas}$ and $τ_{\rm gas}$ than their field counterparts, while remaining on the star forming main sequence. These gas-rich, massive, and star-forming galaxies are predominantly concentrated in the protocluster cores and are likely progenitors of massive ellipticals in the center of today's clusters. These results suggest that the formation of massive galaxies in such environments is sustained by substantial gas reservoirs, which support persistent star formation and drive early mass assembly in forming cluster cores.

astro-ph.GA↗

arXiv2025

MaterialMVP: Illumination-Invariant Material Generation via Multi-view PBR Diffusion

Physically-based rendering (PBR) has become a cornerstone in modern computer graphics, enabling realistic material representation and lighting interactions in 3D scenes. In this paper, we present MaterialMVP, a novel end-to-end model for generating PBR textures from 3D meshes and image prompts, addressing key challenges in multi-view material synthesis. Our approach leverages Reference Attention to extract and encode informative latent from the input reference images, enabling intuitive and controllable texture generation. We also introduce a Consistency-Regularized Training strategy to enforce stability across varying viewpoints and illumination conditions, ensuring illumination-invariant and geometrically consistent results. Additionally, we propose Dual-Channel Material Generation, which separately optimizes albedo and metallic-roughness (MR) textures while maintaining precise spatial alignment with the input images through Multi-Channel Aligned Attention. Learnable material embeddings are further integrated to capture the distinct properties of albedo and MR. Experimental results demonstrate that our model generates PBR textures with realistic behavior across diverse lighting scenarios, outperforming existing methods in both consistency and quality for scalable 3D asset creation.

cs.CV↗

arXiv2025

Long-distance free-space quantum key distribution with continuous variables

Continuous-variable quantum key distribution (CVQKD) enables remote users to share high-rate and unconditionally secure secret keys while maintaining compatibility with classical optical communication networks and effective resistance against background noise. However, CVQKD experiments have only been demonstrated indoors or over short outdoor distances. Here, by developing channel-fluctuation-independent high-precision manipulation of continuous-variable quantum states, high-accuracy quantum signal acquisition and processing, and high-efficiency free-space acquisition, tracking, and pointing technology, we overcome the excess noise due to atmospheric effects especially in daylight without extra wavelength conversion and spectral filtering, and demonstrate for the first time long-distance free-space quantum key distribution over 7-km inland and 9.6-km maritime atmospheric channels with Gaussian-modulated coherent states. This achieved distribution distance of secure quantum secret keys is well beyond the atmosphere's effective thickness, offering a promising alternative for realizing satellite-based quantum cryptography communication in daylight. Moreover, given that the CVQKD system is naturally compatible with existing ground fiber telecommunication networks, it marks an essential step for realizing integrated air-ground quantum access networks with cross-domain applications.

quant-ph↗

arXiv2025

Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges

Due to the remarkable capabilities and growing impact of large language models (LLMs), they have been deeply integrated into many aspects of society. Thus, ensuring their alignment with human values and intentions has emerged as a critical challenge. This survey provides a comprehensive overview of practical alignment techniques, training protocols, and empirical findings in LLM alignment. We analyze the development of alignment methods across diverse paradigms, characterizing the fundamental trade-offs between core alignment objectives. Our analysis shows that while supervised fine-tuning enables basic instruction-following, preference-based methods offer more flexibility for aligning with nuanced human intent. We discuss state-of-the-art techniques, including Direct Preference Optimization (DPO), Constitutional AI, brain-inspired methods, and alignment uncertainty quantification (AUQ), highlighting their approaches to balancing quality and efficiency. We review existing evaluation frameworks and benchmarking datasets, emphasizing limitations such as reward misspecification, distributional robustness, and scalable oversight. We summarize strategies adopted by leading AI labs to illustrate the current state of practice. We conclude by outlining open problems in oversight, value pluralism, robustness, and continuous alignment. This survey aims to inform both researchers and practitioners navigating the evolving landscape of LLM alignment.

cs.AI↗

arXiv2025

AtrousMamaba: An Atrous-Window Scanning Visual State Space Model for Remote Sensing Change Detection

Recently, a novel visual state space (VSS) model, referred to as Mamba, has demonstrated significant progress in modeling long sequences with linear complexity, comparable to Transformer models, thereby enhancing its adaptability for processing visual data. Although most methods aim to enhance the global receptive field by directly modifying Mamba's scanning mechanism, they tend to overlook the critical importance of local information in dense prediction tasks. Additionally, whether Mamba can effectively extract local features as convolutional neural networks (CNNs) do remains an open question that merits further investigation. In this paper, We propose a novel model, AtrousMamba, which effectively balances the extraction of fine-grained local details with the integration of global contextual information. Specifically, our method incorporates an atrous-window selective scan mechanism, enabling a gradual expansion of the scanning range with adjustable rates. This design shortens the distance between adjacent tokens, enabling the model to effectively capture fine-grained local features and global context. By leveraging the atrous window scan visual state space (AWVSS) module, we design dedicated end-to-end Mamba-based frameworks for binary change detection (BCD) and semantic change detection (SCD), referred to as AWMambaBCD and AWMambaSCD, respectively. Experimental results on six benchmark datasets show that the proposed framework outperforms existing CNN-based, Transformer-based, and Mamba-based methods. These findings clearly demonstrate that Mamba not only captures long-range dependencies in visual data but also effectively preserves fine-grained local details.

cs.CV↗

arXiv2025

JWST/MIRI reveals the true number density of massive galaxies in the early Universe

Early JWST studies reporting an unexpected abundance of massive galaxies at $z \sim 5$--$8$ challenge galaxy formation models in the $Λ$CDM framework. Previous stellar mass ($M_\star$) estimates suffered from large uncertainties due to the lack of rest-frame near-infrared data. Using deep JWST/NIRCam and MIRI photometry from PRIMER, we systematically analyze massive galaxies at $z \sim 3$--$8$, leveraging rest-frame $\gtrsim 1\,μ$m constraints. We find MIRI is critical for robust $M_\star$ measurements for massive galaxies at $z > 5$: excluding MIRI overestimates $M_\star$ by $\sim 0.4$ dex on average for $M_\star > 10^{10}\,M_\odot$ galaxies, with no significant effects at lower masses. This reduces number densities of $M_\star > 10^{10}\,M_\odot$ ($10^{10.3}\,M_\odot$) galaxies by $\sim 36\%$ ($55\%$). MIRI inclusion also reduces ``Little Red Dot'' (LRD) contamination in massive galaxy samples, lowering the LRD fraction from $\sim 32\%$ to $\sim 13\%$ at $M_\star > 10^{10.3}\,M_\odot$. Assuming pure stellar origins, LRDs exhibit $M_\star \sim 10^{9\text{--}10.5}\,M_\odot$ with MIRI constraints, rarely exceeding $10^{10.5}\,M_\odot$. Within standard $Λ$CDM, our results indicate a moderate increase in the baryon-to-star conversion efficiency ($ε$) toward higher redshifts and masses at $z > 3$. For the most massive $z \sim 8$ galaxies, $ε\sim 0.3$, compared to $ε\lesssim 0.2$ for typical galaxies at $z < 3$. This result is consistent with models where high gas densities and short free-fall times suppress stellar feedback in massive high-$z$ halos.

astro-ph.GA↗