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The efforts to probe the horizon-scale structure of black holes, such as Event Horizon Telescope and GRAVITY interferometer, might provide valuable insights into the strong-field regime of Einstein's theory of gravity. In the near field region of a black hole, the observational signatures of hotspots could potentially reveal the mechanism causing the flares, or reflect the spacetime geometries. This paper develops a ray tracing scenario to study higher-order images of moving hotspots in a thin disk around a spherical black hole. Our ray-tracing scenario establishes a one-to-one mapping between the emission locations and the observer's sky for each order image. It allows us to have infinite-precision images, because the emission sources are simply projected onto the image plane. Furthermore, we show that a source located anywhere outside the black hole can be repeatedly mapped onto the observer's sky, including primary to arbitrary-order images. For example, the points distributed near the surface of the horizon can all be imaged onto the sky. We investigate the observational signatures of the hotspots, including temporal fluxes and flux centroids, based on images from the primary to the sixth order. These hotspots are considered moving in circular, escape, and terminated orbits for given initial radii and initial azimuth angles. Our results find that the higher-order images can be categorized into two types. Within each type, the temporal fluxes exhibit self-similar tendencies. The fluxes of higher-order images alternately dominate the total flux after the hotspot is swallowed by the black hole, which subsequently result in the total flux decaying with time in an oscillatory manner., Comment: 32 pages, 26 figures

Despite significant advancements in salient object detection(SOD) in optical remote sensing images(ORSI), challenges persist due to the intricate edge structures of ORSIs and the complexity of their contextual relationships. Current deep learning approaches encounter difficulties in accurately identifying boundary features and lack efficiency in collaboratively modeling the foreground and background by leveraging contextual features. To address these challenges, we propose a stronger multifaceted collaborative salient object detector in ORSIs, termed LBA-MCNet, which incorporates aspects of localization, balance, and affinity. The network focuses on accurately locating targets, balancing detailed features, and modeling image-level global context information. Specifically, we design the Edge Feature Adaptive Balancing and Adjusting(EFABA) module for precise edge localization, using edge features to guide attention to boundaries and preserve spatial details. Moreover, we design the Global Distributed Affinity Learning(GDAL) module to model global context. It captures global context by generating an affinity map from the encoders final layer, ensuring effective modeling of global patterns. Additionally, deep supervision during deconvolution further enhances feature representation. Finally, we compared with 28 state of the art approaches on three publicly available datasets. The results clearly demonstrate the superiority of our method.

Pulsar timing array (PTA) collaborations have recently reported evidence of stochastic gravitational waves. Besides gravitational wave signals from cosmological or astrophysical origins, PTAs are also capable of detecting coherently oscillating ultralight dark matter in our galaxy. This paper investigates pulsar timing influenced by ultralight vector dark matter, based on the pure gravitational effect of the vector field. We extensively consider the finite dark matter velocity $v$ for ultralight vector dark matter in PTAs, rather than the limit $v\rightarrow0$ in previous studies. It is found that the leading-order shift in pulsar timing turns out to be proportional to negative slope of $v$. Consequently, one can not distinguish between ultralight vector dark matter and ultralight scalar dark matter with the timing residual amplitude. Assuming the existence of stochastic vector dark matter in our galaxy, the corresponding angular correlation curve in PTAs is found to be similar to that of scalar dark matter. We find that it can be distinguishable, only when the pulsar pairs are distant from the earth., Comment: 22 pages, 7 figures

Given a language expression, referring remote sensing image segmentation (RRSIS) aims to identify the ground objects and assign pixel-wise labels within the imagery. The one of key challenges for this task is to capture discriminative multi-modal features via text-image alignment. However, the existing RRSIS methods use one vanilla and coarse alignment, where the language expression is directly extracted to be fused with the visual features. In this paper, we argue that a "fine-grained image-text alignment" can improve the extraction of multi-modal information. To this point, we here proposed a new referring remote sensing image segmentation method, termed FIANet, that fully exploits the visual and linguistic representations. Specifically, the original referring expression is regarded as context text, which is further decoupled into ground object text and spatial position text. The proposed fine-grained image-text alignment module (FIAM) would simultaneously leverage the features of the input image and the corresponding texts and learn better discriminative multi-modal representation. Meanwhile, to handle the various scales of ground objects in remote sensing, we introduce a Text-aware Multi-scale Enhancement Module (TMEM) to adaptively perform cross-scale fusion and intersections. We evaluate the effectiveness of the proposed methods on two public referring remote sensing datasets including RefSegRS and RRSIS-D, and our method obtains superior performance over several state-of-the-art methods. The code will be publicly available.

We consider a residuals-based distributionally robust optimization model, where the underlying uncertainty depends on both covariate information and our decisions. We adopt regression models to learn the latent decision dependency and construct a nominal distribution (thereby ambiguity sets) around the learned model using empirical residuals from the regressions. Ambiguity sets can be formed via the Wasserstein distance, a sample robust approach, or with the same support as the nominal empirical distribution (e.g., phi-divergences), where both the nominal distribution and the radii of the ambiguity sets could be decision- and covariate-dependent. We provide conditions under which desired statistical properties, such as asymptotic optimality, rates of convergence, and finite sample guarantees, are satisfied. Via cross-validation, we devise data-driven approaches to find the best radii for different ambiguity sets, which can be decision-(in)dependent and covariate-(in)dependent. Through numerical experiments, we illustrate the effectiveness of our approach and the benefits of integrating decision dependency into a residuals-based DRO framework.

In healthcare applications, there is a growing need to develop machine learning models that use data from a single source, such as that from a wrist wearable device, to monitor physical activities, assess health risks, and provide immediate health recommendations or interventions. However, the limitation of using single-source data often compromises the model's accuracy, as it fails to capture the full scope of human activities. While a more comprehensive dataset can be gathered in a lab setting using multiple sensors attached to various body parts, this approach is not practical for everyday use due to the impracticality of wearing multiple sensors. To address this challenge, we introduce a transfer learning framework that optimizes machine learning models for everyday applications by leveraging multi-source data collected in a laboratory setting. We introduce a novel metric to leverage the inherent relationship between these multiple data sources, as they are all paired to capture aspects of the same physical activity. Through numerical experiments, our framework outperforms existing methods in classification accuracy and robustness to noise, offering a promising avenue for the enhancement of daily activity monitoring.

Recent evidence for stochastic gravitational waves reported by pulsar timing array (PTA) collaborations might open a new window for studying cosmology and astrophysical phenomena. In addition to signals from gravitational waves, there is motivation to explore residual signals from oscillating dark matter, which might partially comprise the galactic halo. We investigate fluctuations in pulsar timing originating from the coherent oscillation of scalar dark matter up to the subleading-order correction of $\mathcal{O}(k/m)$, as well as from acoustic oscillations of non-adiabatic perfect fluid dark matter. Both types of dark matter can induce the Newtonian potential and curvature perturbations, thereby affecting pulsar timing. We show distinctive signatures in pulsar timing residuals and angular correlations in the PTA frequency band and considering the known distances of pulsars. For scalar dark matter, both the timing residuals and the angular correlation are sensitive to small variations in the distance, $\delta L$, due to the subleading-order correction of $\mathcal{O}(k/m)$. In contrast, for perfect fluid dark matter, it is insensitive to the $\delta L$. For deterministic source from scalar dark matter, the distance of a pulsar has influence on the degree of directional dependence of timing residuals, significantly. For stochastic source from perfect fluid dark matter, the angular correlation tends to a constant and enhances only when the pulsar pair is very close to each other. In this sense, perfect fluid dark matter is shown to be a more suitable physical origin for monopolar signals in angular correlations compared to the scalar dark matter., Comment: v2: 30 pages, 14 figures, revised version

3D object detection is a fundamental task in scene understanding. Numerous research efforts have been dedicated to better incorporate Hough voting into the 3D object detection pipeline. However, due to the noisy, cluttered, and partial nature of real 3D scans, existing voting-based methods often receive votes from the partial surfaces of individual objects together with severe noises, leading to sub-optimal detection performance. In this work, we focus on the distributional properties of point clouds and formulate the voting process as generating new points in the high-density region of the distribution of object centers. To achieve this, we propose a new method to move random 3D points toward the high-density region of the distribution by estimating the score function of the distribution with a noise conditioned score network. Specifically, we first generate a set of object center proposals to coarsely identify the high-density region of the object center distribution. To estimate the score function, we perturb the generated object center proposals by adding normalized Gaussian noise, and then jointly estimate the score function of all perturbed distributions. Finally, we generate new votes by moving random 3D points to the high-density region of the object center distribution according to the estimated score function. Extensive experiments on two large scale indoor 3D scene datasets, SUN RGB-D and ScanNet V2, demonstrate the superiority of our proposed method. The code will be released at https://github.com/HHrEtvP/DiffVote.

Recent pulsar timing array collaborations have reported evidence of the stochastic gravitational wave background. The gravitational waves induced by primordial curvature perturbations, referred to as scalar-induced gravitational waves (SIGWs), could potentially be the physical origins of this gravitational wave background. In addition to the statistical properties of SIGWs derived from the primordial fluctuations, SIGWs also have intrinsic non-Gaussianity originating from the non-linear interactions of Einstein's gravity, which, however, is rarely explored. In this paper, we study the intrinsic non-Gaussianity of SIGWs with the bispectrum and the skewness under the assumption of the Gaussian primordial curvature perturbations, phenomenologically modeled as a lognormal spectrum. The bispectrum is shown to be vanishing in the collinear limit, which is independent of the initial conditions and the dynamics of SIGWs. For the SIGWs generated during the radiation-dominated era, the bispectrum is of flatten-type non-Gaussianity, with only four polarization components left to be non-vanishing. Additionally, in order to obtain the correct bispectrum, we also propose a time-oscillation average scheme and a regularization scheme. Utilizing the skewness for quantifying the degree of non-Gaussianity, it is found that the curvature power spectrum with a narrow width can result in an enhancement of the third-order non-Gaussianity. The conclusion holds for both the SIGWs generated in the radiation-dominated era and the matter-dominated era., Comment: 31 pages, 11 figures

Starting from more than 11,200 short-period (less than 0.5 days) EW-type eclipsing binary candidates with the All-Sky Automated Survey for Supernovae (ASAS-SN) V-band light curves, we use MCMC and neural networks (NNs) to obtain the mass ratio ($q$), orbital inclination ($incl$), fill-out factor ($f$) and temperature ratio ($T_s/T_p$). After cross-matching with the Gaia DR3 database, the final sample contains parameters of 2,399 A-type and 8,712 W-type contact binaries (CBs). We present the distributions of parameters of these 11,111 short-period CBs. The mass ratio ($q$) and fill-out factor ($f$) are found to obey log-normal distributions, and the remaining parameters obey normal distributions. There is a significant period-temperature correlation of these CBs. Additionally, the temperature ratio (${T_s}$/${T_p}$) tends to increase as the orbital period decreases for W-type CBs. There is no significant correlation between them for A-type CBs. The mass ratio and fill-out factor ($q-f$) diagram suggest there is no significant correlation between these two parameters. A clear correlation exists between the mass ratio and radius ratio. The radius ratio increases with the mass ratio. Moreover, the deep fill-out CBs tend to fall on the upper boundary of the $q$$-$${R_s}$/${R_p}$ distribution, while the shallow fill-out CBs fall on the lower boundary., Comment: 15 pages, 8 figures, 1 table.Accepted for publication in ApJS. The full dataset will be available with the ApJS article

For a long time, due to the high heterogeneity in structure and semantics among various spatiotemporal modal data, the joint interpretation of multimodal spatiotemporal data has been an extremely challenging problem. The primary challenge resides in striking a trade-off between the cohesion and autonomy of diverse modalities, and this trade-off exhibits a progressively nonlinear nature as the number of modalities expands. We introduce the Language as Reference Framework (LaRF), a fundamental principle for constructing a multimodal unified model, aiming to strike a trade-off between the cohesion and autonomy among different modalities. We propose a multimodal spatiotemporal general artificial intelligence model, called AllSpark. Our model integrates thirteen different modalities into a unified framework, including 1D (text, code), 2D (RGB, infrared, SAR, multispectral, hyperspectral, tables, graphs, trajectory, oblique photography), and 3D (point clouds, videos) modalities. To achieve modal cohesion, AllSpark uniformly maps diverse modal features to the language modality. In addition, we design modality-specific prompts to guide multi-modal large language models in accurately perceiving multimodal data. To maintain modality autonomy, AllSpark introduces modality-specific encoders to extract the tokens of various spatiotemporal modalities. And modal bridge is employed to achieve dimensional projection from each modality to the language modality. Finally, observing a gap between the model's interpretation and downstream tasks, we designed task heads to enhance the model's generalization capability on specific downstream tasks. Experiments indicate that AllSpark achieves competitive accuracy in modalities such as RGB and trajectory compared to state-of-the-art models., Comment: 49 pages, 16 tables, 3 figures

Methane (CH4) is the second most critical greenhouse gas after carbon dioxide, contributing to 16-25% of the observed atmospheric warming. Wetlands are the primary natural source of methane emissions globally. However, wetland methane emission estimates from biogeochemistry models contain considerable uncertainty. One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport. Sensitivity Analysis (SA) can help identify critical parameters for methane emission and achieve reduced biases and uncertainties in future projections. This study performs SA for 19 selected parameters responsible for critical biogeochemical processes in the methane module of the Energy Exascale Earth System Model (E3SM) land model (ELM). The impact of these parameters on various CH4 fluxes is examined at 14 FLUXNET- CH4 sites with diverse vegetation types. Given the extensive number of model simulations needed for global variance-based SA, we employ a machine learning (ML) algorithm to emulate the complex behavior of ELM methane biogeochemistry. ML enables the computational time to be shortened significantly from 6 CPU hours to 0.72 milliseconds, achieving reduced computational costs. We found that parameters linked to CH4 production and diffusion generally present the highest sensitivities despite apparent seasonal variation. Comparing simulated emissions from perturbed parameter sets against FLUXNET-CH4 observations revealed that better performances can be achieved at each site compared to the default parameter values. This presents a scope for further improving simulated emissions using parameter calibration with advanced optimization techniques like Bayesian optimization., Comment: 24 pages, 9 figures and 2 tables

With recent advancements in observing supermassive black holes with the Event Horizon Telescope, there has been persistent exploration into what the images can reveal about fundamental physics, including space-time geometries and astrophysical emission sources. Inspired by Penrose's aberration formula for a rigid sphere, which clarified that increased speed does not flatten the appearance of the sphere, we extend the studies to the behavior of the images of accretion emissions. This paper examines the impact of aberration effects on the images of a thin accretion disk around Kerr-de Sitter black holes for finite distant observers, specifically focusing on the primary, secondary, and $n=2$ images. We employ the analytical ray-tracing scenario and extend the astrometric approach to investigate the images in the presence of aberration. This study is non-trivial because we do not assume a specific form of the aberration formula, instead, all aberration effects emerge from a coordinate-independent and tetrad-independent framework referred to as the astrometric approach. Our study finds that the shapes of the lower-order images get highly distorted for finite observers in motion, and the shapes and sizes of primary images are more sensitive to aberration than those of the $n=2$ images. This finding suggests that the primary images could theoretically be distinguished from the shadow based on their distinctive variations., Comment: 33 pages in 11pt, 22 figures

We analyze the energy density spectrum of \acp{SIGW} using the NANOGrav 15-year data set, thereby constraining the primordial non-Gaussian parameter $f_{\mathrm{NL}}$. For the first time, we calculate the seventeen missing two-loop diagrams proportional to $f_{\mathrm{NL}}A_{\zeta}^3$ that correspond to the two-point correlation function $\langle h^{\lambda,(3)}_{\mathbf{k}} h^{\lambda',(2)}_{\mathbf{k}'} \rangle$ for local-type primordial non-Gaussianity. The total energy density spectrum of \acp{SIGW} can be significantly suppressed by these two-loop diagrams. If \acp{SIGW} dominate the \acp{SGWB} observed in \ac{PTA} experiments, the parameter interval $f_{\mathrm{NL}}\in [-5,-1]$ is notably excluded based on NANOGrav 15-year data set. After taking into account abundance of \acp{PBH} and the convergence of the cosmological perturbation expansion, we find that the only possible parameter range for $f_{\mathrm{NL}}$ might be $-1\le f_{\mathrm{NL}}< 0$., Comment: 8 pages, 4 figures

Group-level emotion recognition (GER) is an inseparable part of human behavior analysis, aiming to recognize an overall emotion in a multi-person scene. However, the existing methods are devoted to combing diverse emotion cues while ignoring the inherent uncertainties under unconstrained environments, such as congestion and occlusion occurring within a group. Additionally, since only group-level labels are available, inconsistent emotion predictions among individuals in one group can confuse the network. In this paper, we propose an uncertainty-aware learning (UAL) method to extract more robust representations for GER. By explicitly modeling the uncertainty of each individual, we utilize stochastic embedding drawn from a Gaussian distribution instead of deterministic point embedding. This representation captures the probabilities of different emotions and generates diverse predictions through this stochasticity during the inference stage. Furthermore, uncertainty-sensitive scores are adaptively assigned as the fusion weights of individuals' face within each group. Moreover, we develop an image enhancement module to enhance the model's robustness against severe noise. The overall three-branch model, encompassing face, object, and scene component, is guided by a proportional-weighted fusion strategy and integrates the proposed uncertainty-aware method to produce the final group-level output. Experimental results demonstrate the effectiveness and generalization ability of our method across three widely used databases., Comment: 11 pages,3 figures

Landslide susceptibility assessment (LSA) is of paramount importance in mitigating landslide risks. Recently, there has been a surge in the utilization of data-driven methods for predicting landslide susceptibility due to the growing availability of aerial and satellite data. Nonetheless, the rapid oscillations within the landslide-inducing environment (LIE), primarily due to significant changes in external triggers such as rainfall, pose difficulties for contemporary data-driven LSA methodologies to accommodate LIEs over diverse timespans. This study presents dynamic landslide susceptibility mapping that simply employs multiple predictive models for annual LSA. In practice, this will inevitably encounter small sample problems due to the limited number of landslide samples in certain years. Another concern arises owing to the majority of the existing LSA approaches train black-box models to fit distinct datasets, yet often failing in generalization and providing comprehensive explanations concerning the interactions between input features and predictions. Accordingly, we proposed to meta-learn representations with fast adaptation ability using a few samples and gradient updates; and apply SHAP for each model interpretation and landslide feature permutation. Additionally, we applied MT-InSAR for LSA result enhancement and validation. The chosen study area is Lantau Island, Hong Kong, where we conducted a comprehensive dynamic LSA spanning from 1992 to 2019. The model interpretation results demonstrate that the primary factors responsible for triggering landslides in Lantau Island are terrain slope and extreme rainfall. The results also indicate that the variation in landslide causes can be primarily attributed to extreme rainfall events, which result from global climate change, and the implementation of the Landslip Prevention and Mitigation Programme (LPMitP) by the Hong Kong government.

Our work presents a novel spectrum-inspired learning-based approach for generating clothing deformations with dynamic effects and personalized details. Existing methods in the field of clothing animation are limited to either static behavior or specific network models for individual garments, which hinders their applicability in real-world scenarios where diverse animated garments are required. Our proposed method overcomes these limitations by providing a unified framework that predicts dynamic behavior for different garments with arbitrary topology and looseness, resulting in versatile and realistic deformations. First, we observe that the problem of bias towards low frequency always hampers supervised learning and leads to overly smooth deformations. To address this issue, we introduce a frequency-control strategy from a spectral perspective that enhances the generation of high-frequency details of the deformation. In addition, to make the network highly generalizable and able to learn various clothing deformations effectively, we propose a spectral descriptor to achieve a generalized description of the global shape information. Building on the above strategies, we develop a dynamic clothing deformation estimator that integrates frequency-controllable attention mechanisms with long short-term memory. The estimator takes as input expressive features from garments and human bodies, allowing it to automatically output continuous deformations for diverse clothing types, independent of mesh topology or vertex count. Finally, we present a neural collision handling method to further enhance the realism of garments. Our experimental results demonstrate the effectiveness of our approach on a variety of free-swinging garments and its superiority over state-of-the-art methods.

Pulsar timing array (PTA) data releases showed strong evidence for a stochastic gravitational-wave background in the nanohertz band. When the signal is interpreted by a scenario of scalar-induced gravitational waves (SIGWs), we encounter overproduction of primordial black holes (PBHs). We wonder if varying the equation of state (EoS) of the early Universe can resolve this issue and thereby lead to a consistent interpretation of the PTA data. Analyzing a data combination of PTA, big-bang nucleosynthesis, and cosmic microwave background, we find that an epoch with EoS $w\sim\mathcal{O}(10^{-2})$ between the end of inflation and the onset of radiation domination can significantly suppress the production of PBHs, leading to alleviation of the PBH-overproduction issue. With the inferred interval $w=0.44_{-0.40}^{+0.52}$ at 95\% confidence level, our scenario can interpret the PTA data just as well as the conventional scenario of SIGWs produced during the radiation domination., Comment: 9 pages, 5 figures, 2 columns, minor revision. To be published in Chinese Physics C

Recently, strong evidence for a gravitational wave background has been reported by collaborations of pulsar timing arrays (PTA). In the framework of scalar-induced gravitational waves (SIGWs), we concurrently investigate the second and third order gravitational waves by jointly analyzing PTA data, alongside big-bang nucleosynthesis (BBN), and cosmic microwave background (CMB) datasets. We determine the primordial curvature spectral amplitude as $0.021

We investigate the second order energy density perturbation $\delta^{(2)}$ induced by small-scale Gaussian and local-type non-Gaussian primordial curvature perturbations. The relative abundance of primordial black hole is calculated in terms of the probability density function of total energy density perturbation $\delta_r=\delta^{(1)}+\frac{1}{2}\delta^{(2)}$. The effects of second order density perturbation greatly reduce the upper bounds of small-scale power spectra of primordial curvature perturbations by one to two orders of magnitude. For log-normal primordial power spectrum, its amplitude $A_{\zeta}$ is constrained to be about $A_{\zeta}\sim 3\times10^{-3}$. And for local-type non-Gaussianity with $f_{\mathrm{NL}}=10$, the upper bound of $A_{\zeta}$ is about $2.5\times10^{-4}$., Comment: 5 pages, 4 figures

Multiple pulsar-timing-array collaborations have reported strong evidence for the existence of a gravitational-wave background. We study physical implications of this signal for cosmology, assuming that it is attributed to scalar-induced gravitational waves. By incorporating primordial non-Gaussianity $f_{\mathrm{NL}}$, we specifically examine the nature of primordial curvature perturbations and primordial black holes. We find that the signal allows for a primordial non-Gaussianity $f_{\mathrm{NL}}$ in the range of $-4.1\lesssim f_{\mathrm{NL}} \lesssim 4.1$ (68\% confidence intervals) and a mass range for primordial black holes $m_{\mathrm{pbh}}$ spanning from $\sim10^{-5}M_{\odot}$ to $\sim10^{-2}M_{\odot}$. Furthermore, we find that the signal favors a negative non-Gaussianity, which can suppress the abundance of primordial black holes. We also demonstrate that the anisotropies of scalar-induced gravitational waves serve as a powerful tool to probe the non-Gaussianity $f_{\mathrm{NL}}$. We conduct a comprehensive analysis of the angular power spectrum within the nano-Hertz band. Looking ahead, we anticipate that future projects, such as the Square Kilometre Array, will have the potential to measure these anisotropies and provide further insights into the primordial universe., Comment: Major revisions, 2 columns, 8 pages, 4 figures, 1 appendix

Shocks are abundant in star-forming regions, and are often related with star formation. In our previous observations toward 100 starless clump candidates (SCCs) in the Galaxy, a sample of 34 SCCs associated with shocks is identified. In this work, we perform mapping observations of the SiO 2-1, 3-2, HC$_3$N 10-9, HCO$^+$ 1-0, H$^{13}$CO$^+$ 1-0, and H41$\alpha$ lines toward 9 out of the detected sources by using IRAM 30-m radio telescope to study the origins of the shocks in the SCCs. We find shocks in three sources (BGPS 3110, 3114, and 3118) are produced by collisions between the expanding ionized gas and ambient molecular gas, instead of by the star formation activity inside SCCs. On the other hand, shocks in the other six sources are related to star formation activity of SCCs. The signatures of protostellar outflows are clearly shown in the molecular lines toward BGPS 4029, 4472, 5064. Comparing our results with the previous ALMA observations performed in the same region, the shocks in BGPS 3686 and 5114 are also likely to be due to protostellar activity. The origin of shock in BGPS 5243 is still unclear although some features in the SiO spectra imply the presence of protostellar activity., Comment: 21 pages, 21 figues

Massive stars are formed in molecular clouds, and produce H II regions when they evolve onto the main sequence. The expansion of H II region can both suppress and promote star formation in the vicinity. M17 H II region is a giant cometary H II region near many massive clumps containing starless and protostellar sources. It is an appropriate target to study the effect of feedback from previously formed massive stars on the nearby star-forming environments. Observations of SiO 2-1, HCO$^+$ 1-0, H$^{13}$CO$^+$ 1-0, HC$_3$N 10-9, and H41$\alpha$ lines are performed toward M17 H II region with ambient candidates of massive clumps. In the observations, the widespread shocked gas surrounding M17 H II region is detected: it probably originates from the collision between the expanding ionized gas and the ambient neutral medium. Some massive clumps are found in the overlap region of the shock and dense-gas tracing lines while the central velocities of shocked and high-density gases are similar. This suggests that part of massive clumps are located in the shell of H II region, and may be formed from the accumulated neutral materials in the shell. In addition, by comparing the observations toward M17 H II region with the simulation of cometary H II region, we infer the presence of one or more massive stars travelling at supersonic velocity with respect to the natal molecular cloud in the H II region., Comment: 13 pages, 11 figures

The lack of fa\c{c}ade structures in photogrammetric mesh models renders them inadequate for meeting the demands of intricate applications. Moreover, these mesh models exhibit irregular surfaces with considerable geometric noise and texture quality imperfections, making the restoration of structures challenging. To address these shortcomings, we present StructuredMesh, a novel approach for reconstructing fa\c{c}ade structures conforming to the regularity of buildings within photogrammetric mesh models. Our method involves capturing multi-view color and depth images of the building model using a virtual camera and employing a deep learning object detection pipeline to semi-automatically extract the bounding boxes of fa\c{c}ade components such as windows, doors, and balconies from the color image. We then utilize the depth image to remap these boxes into 3D space, generating an initial fa\c{c}ade layout. Leveraging architectural knowledge, we apply binary integer programming (BIP) to optimize the 3D layout's structure, encompassing the positions, orientations, and sizes of all components. The refined layout subsequently informs fa\c{c}ade modeling through instance replacement. We conducted experiments utilizing building mesh models from three distinct datasets, demonstrating the adaptability, robustness, and noise resistance of our proposed methodology. Furthermore, our 3D layout evaluation metrics reveal that the optimized layout enhances precision, recall, and F-score by 6.5%, 4.5%, and 5.5%, respectively, in comparison to the initial layout., Comment: 30 pages,15 figures