arXiv subjects

Bo Zhang

Publications and source records attributed to Bo Zhang.

At least 397 records · Page 22Linked to original sources

arXiv2024

Asteroseismological analysis of the non-Blazhko RRab star EPIC~248846335 in LAMOST -- Kepler$/$ K2 project

We conduct an asteroseismological analysis on the non-Blazhko ab-type RR Lyrae star EPIC 248846335 employing the Radial Stellar Pulsations (RSP) module of the Modules for Experiments in Stellar Astrophysics (MESA) based on the set of stellar parameters. The atmospheric parameters as $T_\mathrm{eff}$ = 6933$\pm$70 $K$, log $g$ = 3.35$\pm$ 0.50 and [Fe/H] = -1.18 $\pm$ 0.14 are estimated from the Low-Resolution Spectra of LAMOST DR9. The luminosity $L$ = 49.70$_{-1.80}^{+2.99}$ $L_\odot$ and mass M = 0.56 $\pm$ 0.07 $M_\odot$ are calculated, respectively, using the distance provided by Gaia and the metallicity estimated from the Low-Resolution Spectra. The Fourier parameters of the light curves observed by $K2$ and RV curves determined from the Medium-Resolution Spectra of LAMOST DR10 are also calculated in this work. The period of the fundamental mode of the star and the residuals $r$ of the Fourier parameters between the models and observations serve to select optimal model, whose stellar parameters are $T_\mathrm{eff}$ = 6700 $\pm$ 220 K, log $g$ = 2.70, [Fe/H] = -1.20 $\pm$ 0.2, M = 0.59 $\pm$ 0.05 $M_\odot$, and $L$ = 56.0 $\pm$ 4.2 $L_\odot$. The projection factors are constrained as 1.20 $\pm$ 0.02 and 1.59 $\pm$ 0.13 by the blue- and red-arm observed velocities with their corresponding RV curves derived from the best-fit model, respectively. The precise determination of stellar parameters in ab-type RR Lyrae stars is crucial for understanding the physical processes that occur during pulsation and for providing a deeper understanding of its Period-Luminosity relationship.

astro-ph.SR↗

arXiv2024

Once for Both: Single Stage of Importance and Sparsity Search for Vision Transformer Compression

Recent Vision Transformer Compression (VTC) works mainly follow a two-stage scheme, where the importance score of each model unit is first evaluated or preset in each submodule, followed by the sparsity score evaluation according to the target sparsity constraint. Such a separate evaluation process induces the gap between importance and sparsity score distributions, thus causing high search costs for VTC. In this work, for the first time, we investigate how to integrate the evaluations of importance and sparsity scores into a single stage, searching the optimal subnets in an efficient manner. Specifically, we present OFB, a cost-efficient approach that simultaneously evaluates both importance and sparsity scores, termed Once for Both (OFB), for VTC. First, a bi-mask scheme is developed by entangling the importance score and the differentiable sparsity score to jointly determine the pruning potential (prunability) of each unit. Such a bi-mask search strategy is further used together with a proposed adaptive one-hot loss to realize the progressive-and-efficient search for the most important subnet. Finally, Progressive Masked Image Modeling (PMIM) is proposed to regularize the feature space to be more representative during the search process, which may be degraded by the dimension reduction. Extensive experiments demonstrate that OFB can achieve superior compression performance over state-of-the-art searching-based and pruning-based methods under various Vision Transformer architectures, meanwhile promoting search efficiency significantly, e.g., costing one GPU search day for the compression of DeiT-S on ImageNet-1K.

cs.CV↗

arXiv2024

Shadow Generation for Composite Image Using Diffusion model

In the realm of image composition, generating realistic shadow for the inserted foreground remains a formidable challenge. Previous works have developed image-to-image translation models which are trained on paired training data. However, they are struggling to generate shadows with accurate shapes and intensities, hindered by data scarcity and inherent task complexity. In this paper, we resort to foundation model with rich prior knowledge of natural shadow images. Specifically, we first adapt ControlNet to our task and then propose intensity modulation modules to improve the shadow intensity. Moreover, we extend the small-scale DESOBA dataset to DESOBAv2 using a novel data acquisition pipeline. Experimental results on both DESOBA and DESOBAv2 datasets as well as real composite images demonstrate the superior capability of our model for shadow generation task. The dataset, code, and model are released at https://github.com/bcmi/Object-Shadow-Generation-Dataset-DESOBAv2.

cs.CV↗

arXiv2024

TYC 3340-2437-1: A Quadruple System with A Massive Star

Hierarchical massive quadruple systems are ideal laboratories for examining the theories of star formation, dynamical evolution, and stellar evolution. The successive mergers of hierarchical quadruple systems might explain the mass gap between neutron stars and black holes. Looking for light curves of O-type binaries identified by LAMOST, we find a (2+2) quadruple system: TYC 3340-2437-1, located in the stellar bow-shock nebula (SBN). It has a probability of over 99.99\% being a quadruple system derived from the surface density of the vicinity stars. Its inner orbital periods are 3.390602(89) days and 2.4378(16) days, respectively, and the total mass is about (11.47 + 5.79) + (5.2 + 2.02) = 24.48 $M_{\odot}$. The line-of-sight inclinations of the inner binaries, B$_1$ and B$_2$, are 55.94 and 78.2 degrees, respectively, indicating that they are not co-planar. Based on observations spanning 34 months and the significance of the astrometric excess noise ($D>2$) in Gaia DR3 data, we guess that its outer orbital period might be a few years. If it were true, the quadruple system might form through the disk fragmentation mechanism with outer eccentric greater than zero. This eccentricity could be the cause of both the arc-like feature of the SBN and the noncoplanarity of the inner orbit. The outer orbital period and outer eccentric could be determined with the release of future epoch astrometric data of Gaia.

astro-ph.SR↗

arXiv2024

mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding

Structure information is critical for understanding the semantics of text-rich images, such as documents, tables, and charts. Existing Multimodal Large Language Models (MLLMs) for Visual Document Understanding are equipped with text recognition ability but lack general structure understanding abilities for text-rich document images. In this work, we emphasize the importance of structure information in Visual Document Understanding and propose the Unified Structure Learning to boost the performance of MLLMs. Our Unified Structure Learning comprises structure-aware parsing tasks and multi-grained text localization tasks across 5 domains: document, webpage, table, chart, and natural image. To better encode structure information, we design a simple and effective vision-to-text module H-Reducer, which can not only maintain the layout information but also reduce the length of visual features by merging horizontal adjacent patches through convolution, enabling the LLM to understand high-resolution images more efficiently. Furthermore, by constructing structure-aware text sequences and multi-grained pairs of texts and bounding boxes for publicly available text-rich images, we build a comprehensive training set DocStruct4M to support structure learning. Finally, we construct a small but high-quality reasoning tuning dataset DocReason25K to trigger the detailed explanation ability in the document domain. Our model DocOwl 1.5 achieves state-of-the-art performance on 10 visual document understanding benchmarks, improving the SOTA performance of MLLMs with a 7B LLM by more than 10 points in 5/10 benchmarks. Our codes, models, and datasets are publicly available at https://github.com/X-PLUG/mPLUG-DocOwl/tree/main/DocOwl1.5.

cs.CV↗

arXiv2024

Increasing stability for inverse acoustic source problems in the time domain

This paper is concerned with inverse source problems for the acoustic wave equation in the full space R^3, where the source term is compactly supported in both time and spatial variables. The main goal is to investigate increasing stability for the wave equation in terms of the interval length of given parameters (e.g., bandwith of the temporal component of the source function). We establish increasing stability estimates of the L^2 -norm of the source function by using only the Dirichlet boundary data. Our method relies on the Huygens principle, the Fourier transform and explicit bounds for the continuation of analytic functions.

math.AP↗

arXiv2024

DeepSeek-VL: Towards Real-World Vision-Language Understanding

We present DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. Our approach is structured around three key dimensions: We strive to ensure our data is diverse, scalable, and extensively covers real-world scenarios including web screenshots, PDFs, OCR, charts, and knowledge-based content, aiming for a comprehensive representation of practical contexts. Further, we create a use case taxonomy from real user scenarios and construct an instruction tuning dataset accordingly. The fine-tuning with this dataset substantially improves the model's user experience in practical applications. Considering efficiency and the demands of most real-world scenarios, DeepSeek-VL incorporates a hybrid vision encoder that efficiently processes high-resolution images (1024 x 1024), while maintaining a relatively low computational overhead. This design choice ensures the model's ability to capture critical semantic and detailed information across various visual tasks. We posit that a proficient Vision-Language Model should, foremost, possess strong language abilities. To ensure the preservation of LLM capabilities during pretraining, we investigate an effective VL pretraining strategy by integrating LLM training from the beginning and carefully managing the competitive dynamics observed between vision and language modalities. The DeepSeek-VL family (both 1.3B and 7B models) showcases superior user experiences as a vision-language chatbot in real-world applications, achieving state-of-the-art or competitive performance across a wide range of visual-language benchmarks at the same model size while maintaining robust performance on language-centric benchmarks. We have made both 1.3B and 7B models publicly accessible to foster innovations based on this foundation model.

cs.AI↗

arXiv2024

First-principles Study of Carrier Mobility in MX (M=Sn, Pb; X=P, As) Monolayers

Compounds from groups IV and V have been the focus of recent research due to their impressive physical characteristics and structural stability. In this study, the MX monolayers (M=Sn, Pb; N=P, As) are investigated with first-principles calculations based on Boltzmann transport theory. The results show that SnP, SnAs, and PbAs all exhibit indirect band gaps, whereas PbP is the only semiconductor with a direct band gap. One important finding is that intravalley scattering has a significant impact on electron-phonon coupling. Interestingly, changes in carrier concentration do not affect the electron mobility within these MX monolayers, with SnP exhibiting the highest electron mobility among them. Subsequently, the SnP under a 6% biaxial strain is further explored and the results demonstrated a considerable increase in electron mobility to 2,511.9 cm^2/Vs, which is attributable to decreased scattering. This suggests that MX monolayers, especially SnP, are promising options for 2D semiconductor materials in the future.

cond-mat.mtrl-sci↗

arXiv2024

Language-Driven Anchors for Zero-Shot Adversarial Robustness

Deep Neural Networks (DNNs) are known to be susceptible to adversarial attacks. Previous researches mainly focus on improving adversarial robustness in the fully supervised setting, leaving the challenging domain of zero-shot adversarial robustness an open question. In this work, we investigate this domain by leveraging the recent advances in large vision-language models, such as CLIP, to introduce zero-shot adversarial robustness to DNNs. We propose LAAT, a Language-driven, Anchor-based Adversarial Training strategy. LAAT utilizes the features of a text encoder for each category as fixed anchors (normalized feature embeddings) for each category, which are then employed for adversarial training. By leveraging the semantic consistency of the text encoders, LAAT aims to enhance the adversarial robustness of the image model on novel categories. However, naively using text encoders leads to poor results. Through analysis, we identified the issue to be the high cosine similarity between text encoders. We then design an expansion algorithm and an alignment cross-entropy loss to alleviate the problem. Our experimental results demonstrated that LAAT significantly improves zero-shot adversarial robustness over state-of-the-art methods. LAAT has the potential to enhance adversarial robustness by large-scale multimodal models, especially when labeled data is unavailable during training.

cs.CV↗

arXiv2024

GMRT observations of OHM candidates from the ALFALFA survey

We present the results of our observations using the Giant Meterwave Radio Telescope (GMRT) to investigate the radio continuum and OH line emission of 10 OHM candidates from the Arecibo Legacy Fast ALFA (ALFALFA) survey. Among these candidates, we have identified two sources, AGC115713 and AGC249507, which display compact OH line emission that are spatially associated with radio continuum emission. These characteristics align with the typical properties of OHM galaxies. Furthermore, the infrared (IR) properties of these two galaxies are consistent with those of known OHM galaxies. %Importantly, these two sources have been independently confirmed by alternative methods. Of the two sources, AGC 249507 has been confirmed through optical redshift, whereas AGC 115713 meets a WISE color selection criterion in the literature, providing additional support for this source being an OHM galaxy rather than a nearby \HI galaxy. On the contrary, no significant spectral line emission were detected in the remaining eight OHM candidates using our full GMRT dataset. This suggests that the spectral line emission initially detected by the ALFALFA survey may have been significantly resolved in our high-resolution observations. Additionally, the absence of radio continuum emission in 6 candidates also distinguishes them from known OHM galaxies documented in the literature. These findings support the notion that OHM emission may be distributed on a subarcsecond scale, underscoring the utility of arcsecond-scale observations in confirming OHM candidates, particularly those lacking optical redshift data.

astro-ph.GA↗

arXiv2024

The role of randomization inference in unraveling individual treatment effects in early phase vaccine trials

Randomization inference is a powerful tool in early phase vaccine trials when estimating the causal effect of a regimen against a placebo or another regimen. Randomization-based inference often focuses on testing either Fisher's sharp null hypothesis of no treatment effect for any participant or Neyman's weak null hypothesis of no sample average treatment effect. Many recent efforts have explored conducting exact randomization-based inference for other summaries of the treatment effect profile, for instance, quantiles of the treatment effect distribution function. In this article, we systematically review methods that conduct exact, randomization-based inference for quantiles of individual treatment effects (ITEs) and extend some results to a special case where naïve participants are expected not to exhibit responses to highly specific endpoints. These methods are suitable for completely randomized trials, stratified completely randomized trials, and a matched study comparing two non-randomized arms from possibly different trials. We evaluate the usefulness of these methods using synthetic data in simulation studies. Finally, we apply these methods to HIV Vaccine Trials Network Study 086 (HVTN 086) and HVTN 205 and showcase a wide range of application scenarios of the methods. R code that replicates all analyses in this article can be found in first author's GitHub page at https://github.com/Zhe-Chen-1999/ITE-Inference.

stat.ME↗

arXiv2024

Estimating the Causal Effect of Early ArXiving on Paper Acceptance

What is the effect of releasing a preprint of a paper before it is submitted for peer review? No randomized controlled trial has been conducted, so we turn to observational data to answer this question. We use data from the ICLR conference (2018--2022) and apply methods from causal inference to estimate the effect of arXiving a paper before the reviewing period (early arXiving) on its acceptance to the conference. Adjusting for confounders such as topic, authors, and quality, we may estimate the causal effect. However, since quality is a challenging construct to estimate, we use the negative outcome control method, using paper citation count as a control variable to debias the quality confounding effect. Our results suggest that early arXiving may have a small effect on a paper's chances of acceptance. However, this effect (when existing) does not differ significantly across different groups of authors, as grouped by author citation count and institute rank. This suggests that early arXiving does not provide an advantage to any particular group.

cs.CL↗

arXiv2024

MobileVLM V2: Faster and Stronger Baseline for Vision Language Model

We introduce MobileVLM V2, a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs' performance. Specifically, MobileVLM V2 1.7B achieves better or on-par performance on standard VLM benchmarks compared with much larger VLMs at the 3B scale. Notably, our 3B model outperforms a large variety of VLMs at the 7B+ scale. Our models will be released at https://github.com/Meituan-AutoML/MobileVLM .

cs.CV↗

arXiv2024

OASim: an Open and Adaptive Simulator based on Neural Rendering for Autonomous Driving

With deep learning and computer vision technology development, autonomous driving provides new solutions to improve traffic safety and efficiency. The importance of building high-quality datasets is self-evident, especially with the rise of end-to-end autonomous driving algorithms in recent years. Data plays a core role in the algorithm closed-loop system. However, collecting real-world data is expensive, time-consuming, and unsafe. With the development of implicit rendering technology and in-depth research on using generative models to produce data at scale, we propose OASim, an open and adaptive simulator and autonomous driving data generator based on implicit neural rendering. It has the following characteristics: (1) High-quality scene reconstruction through neural implicit surface reconstruction technology. (2) Trajectory editing of the ego vehicle and participating vehicles. (3) Rich vehicle model library that can be freely selected and inserted into the scene. (4) Rich sensors model library where you can select specified sensors to generate data. (5) A highly customizable data generation system can generate data according to user needs. We demonstrate the high quality and fidelity of the generated data through perception performance evaluation on the Carla simulator and real-world data acquisition. Code is available at https://github.com/PJLab-ADG/OASim.

cs.CV↗

arXiv2024

HiFAST: an HI data calibration and imaging pipeline for FAST

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) has the largest aperture and a 19-beam L-band receiver, making it powerful for investigating the neutral hydrogen atomic gas (HI) in the universe. We present HiFAST (https://hifast.readthedocs.io), a dedicated, modular, and self-contained calibration and imaging pipeline for processing the HI data of FAST. The pipeline consists of frequency-dependent noise diode calibration, baseline fitting, standing wave removal using an FFT-based method, flux density calibration, stray radiation correction, and gridding to produce data cubes. These modules can be combined as needed to process the data from most FAST observation modes: tracking, drift scanning, On-The-Fly mapping, and most of their variants. With HiFAST, the RMS noises of the calibrated spectra from all 19 beams were only slightly (~ 5%) higher than the theoretical expectation. The results for the extended source M33 and the point sources are consistent with the results from Arecibo. The moment maps (0,1 and 2) of M33 agree well with the results from the Arecibo Galaxy Environment Survey (AGES) with a fractional difference of less than 10%. For a common sample of 221 sources with signal-to-noise ratio S/N >10 from the Arecibo Legacy Fast ALFA (ALFALFA) survey, the mean value of fractional difference in the integrated flux density, $S_{\mathrm{int}}$, between the two datasets is approximately 0.005 %, with a dispersion of 15.4%. Further checks on the integrated flux density of 23 sources with seven observations indicate that the variance in the flux density of the source with luminous objects ($S_\mathrm{int}$ $ > 2.5$ Jy km s$^{-1}$) is less than 5%. Our tests suggest that the FAST telescope, with the efficient, precise, and user-friendly pipeline HiFAST, will yield numerous significant scientific findings in the investigation of the HI in the universe.

astro-ph.GA↗

arXiv2024

Evolved Massive Stars at Low-metallicity VII. the Lower Mass Limit of Red Supergiant Population in the Large Magellanic Cloud

The precise definition of the lower mass limit of red supergiant stars (RSGs) is an open question in astrophysics and does not attract too much attention. Here we assemble a spectroscopic evolved cool star sample with 6,602 targets, including RSGs, asymptotic giant branch stars, and red giant branch stars, in the Large Magellanic Cloud based on \textit{Gaia} DR3 and SDSS-IV/APOGEE-2. The reference spectrum of each stellar population is built according to the quantile range of relative intensity ($1\%\sim99\%$). Five different methods, e.g., chi-square ($χ^2$), cosine similarity (CS), machine learning (ML), equivalent width (EW), and line ratio (LR), are used in order to separate different stellar populations. The ML and $χ^2$ provide the best and relatively consistent prediction of certain population. The derived lower limit of the RSG population is able to reach to the $\rm K_S$-band tip of red giant branch ($\rm K_S~$$\approx12.0$ mag), indicating a luminosity as low as about $10^{3.5}~L_{\sun}$, which corresponds to a stellar radius only about $100~R_{\sun}$. Given the mass-luminosity relation of $L/L_\sun =f(M/M_\sun)^3$ with $f\approx15.5\pm3$ and taking into account of the mass loss of faint RSGs up to now, the minimal initial mass of the RSG population would be about $6.1\pm0.4~M_\sun$, which is much lower than the traditional threshold of $8~M_\sun$ for the massive stars. This is the first spectroscopic evidence, indicating that the lower mass limit of RSG population is around $6~M_\sun$. However, the destinies of such faint RSGs are still elusive and may have large impact on the stellar evolutionary and supernova models.

astro-ph.SR↗

arXiv2024

LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection

Due to highly constrained computing power and memory, deploying 3D lidar-based detectors on edge devices equipped in autonomous vehicles and robots poses a crucial challenge. Being a convenient and straightforward model compression approach, Post-Training Quantization (PTQ) has been widely adopted in 2D vision tasks. However, applying it directly to 3D lidar-based tasks inevitably leads to performance degradation. As a remedy, we propose an effective PTQ method called LiDAR-PTQ, which is particularly curated for 3D lidar detection (both SPConv-based and SPConv-free). Our LiDAR-PTQ features three main components, \textbf{(1)} a sparsity-based calibration method to determine the initialization of quantization parameters, \textbf{(2)} a Task-guided Global Positive Loss (TGPL) to reduce the disparity between the final predictions before and after quantization, \textbf{(3)} an adaptive rounding-to-nearest operation to minimize the layerwise reconstruction error. Extensive experiments demonstrate that our LiDAR-PTQ can achieve state-of-the-art quantization performance when applied to CenterPoint (both Pillar-based and Voxel-based). To our knowledge, for the very first time in lidar-based 3D detection tasks, the PTQ INT8 model's accuracy is almost the same as the FP32 model while enjoying $3\times$ inference speedup. Moreover, our LiDAR-PTQ is cost-effective being $30\times$ faster than the quantization-aware training method. Code will be released at \url{https://github.com/StiphyJay/LiDAR-PTQ}.

cs.CV↗

arXiv2024

ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation

Domain shifts such as sensor type changes and geographical situation variations are prevalent in Autonomous Driving (AD), which poses a challenge since AD model relying on the previous domain knowledge can be hardly directly deployed to a new domain without additional costs. In this paper, we provide a new perspective and approach of alleviating the domain shifts, by proposing a Reconstruction-Simulation-Perception (ReSimAD) scheme. Specifically, the implicit reconstruction process is based on the knowledge from the previous old domain, aiming to convert the domain-related knowledge into domain-invariant representations, e.g., 3D scene-level meshes. Besides, the point clouds simulation process of multiple new domains is conditioned on the above reconstructed 3D meshes, where the target-domain-like simulation samples can be obtained, thus reducing the cost of collecting and annotating new-domain data for the subsequent perception process. For experiments, we consider different cross-domain situations such as Waymo-to-KITTI, Waymo-to-nuScenes, Waymo-to-ONCE, etc, to verify the zero-shot target-domain perception using ReSimAD. Results demonstrate that our method is beneficial to boost the domain generalization ability, even promising for 3D pre-training.

cs.CV↗