Density Maps Research Articles

Estimating Daily Snow Density Through a Spatiotemporal Random Forest Model

AbstractSnow density is of paramount importance in water resource management, snow avalanche warning, and climate change research. However, the lack of competent methods for long‐term and vast‐scale snow density mapping persists due to the intricate spatiotemporal dependencies inherent in snow density, resulting in scarce and inaccurate snow density products. To address this challenge, a spatiotemporal random forest (STRF) model is constructed by leveraging in‐situ measurements, multisource remote sensing, and reanalysis data. It tackles the spatiotemporal dependencies in snow density arising from its inherent heterogeneity and the relations involving snow density and nonlinearly connected meteorological, terrain, vegetation, and snow‐related factors. The effectiveness of the model is substantiated through rigorous validation methods, including random, temporal, and spatial block cross‐validations as well as independent validation, apparently surpassing ERA5‐Land snow density. The estimated snow density is also demonstrated to be able to improve existing snow water equivalent data set using fixed snow density. Utilizing the proposed model, a data set of daily 25‐km snow density from 1980 to 2018 is constructed for stable snow cover areas in China, which holds significant potential for research and applications in the realm of snow hydrology.

Neighborhood mobility and racial disparities in preterm birth: A sibling study in California

Recent work finds that upward neighborhood mobility—defined as reductions in neighborhood socioeconomic disadvantage due to moving—may improve birth outcomes. Less work, however, explores whether changes in socioeconomic context differentially impact birth outcomes by maternal race and ethnicity. In the US, mothers of minoritized racial and ethnic identity often experience worse neighborhood conditions and pregnancy outcomes than White mothers. Using a sibling-linked dataset, we examined whether neighborhood mobility corresponds with changes in preterm birth risk among Asian (N = 130,079), Black (N = 50,149), Hispanic (N = 429,938), and White (N = 233,428) mothers who delivered multiple live births in California between 2005 and 2015. We linked residential addresses at each birth to census-derived indices of neighborhood disadvantage and defined levels of neighborhood mobility as moving-induced changes in disadvantage between pregnancies. We mapped neighborhood mobility patterns and fit conditional logistic regression models estimating the odds of preterm birth in the sibling delivered after moving, controlling for the risk of preterm birth in the sibling delivered before moving, by maternal race and ethnicity. Dot density maps highlight racialized patterns of neighborhood mobility and segregation between Black and White mothers. Regression results show that Black and, in some cases, Asian and Hispanic mothers who experienced upward mobility (moves away from neighborhood disadvantage) exhibited reduced odds of preterm birth in the second delivery. Upward mobility did not reduce the odds of preterm birth among White mothers. Findings suggest that policies and programs that enable opportunities for neighborhood mobility may reduce persistent racial and ethnic disparities in adverse birth outcomes.

Poster 218: Localized Acetabular Bone Density Distributions Are Altered in Unilateral Femoral Acetabular Impingement

Objectives: Femoral acetabular impingement (FAI) can lead to bony and cartilage changes at the joint, and it has been suggested that changes in bone mineral density in the acetabulum may correlate with osteoarthritic degeneration of the hip. In this study, we aimed to use opportunistic quantitative computed tomography (QCT) to map density distributions across the acetabular region in patients with unilateral FAI and determine if there were differences between the affected and unaffected side. Furthermore, we assessed if these differences in density distributions were associated with radiographic measures of FAI. Methods: Twenty patients (age 31.9 (SD 9.2), 13 female) who underwent surgery for unilateral FAI and had an associated CT scan of both hips were included in this retrospective study. Both sides of the pelvis were segmented, and 3D models were created. Estimated bone density values were mapped across the bone models via QCT. A thin-spline semi-landmarking approach was used to allow density values at equivalent anatomical points to be compared within patients and across the group. With the contralateral side used as the control, differences in bone density between sides were calculated. A mixed effect linear regression model was used to determine if there were associations between the localized bone density and radiographic measures of Tonnis grade, alpha angle, lateral center edge angle (LCEA), and cross over sign, while controlling for age and sex. To visualize the data, density changes and statistical results were color-mapped to the bone models. Results: Overall, several regions of the acetabulum were found to have increased bone density in the affected side compared to the contralateral (Figure 1). Specifically, the anterior-superior parts of the acetabular rim appeared to be most affected, along with the posterosuperior region (Figure 2). Limited associations were found between localized density and the clinical variables, with the most notable finding being a region of the superior rim that was found to be associated with the alpha angle (Figure 3). Conclusions: FAI leads to distinct patterns of bone density changes around the acetabulum, although there is still variability at the level of the individual patient. It is unclear whether these changes in bone density may be related to the underlying condition, alterations in the patient’s mechanics due to pain, or a combination of these factors. Providing detailed density maps of the bony anatomy may also assist surgical providers in planning interventions. Further research may elucidate the relationship between bone density changes and long-term degeneration of the joint.

De novo atomic protein structure modeling for cryoEM density maps using 3D transformer and HMM

Accurately building 3D atomic structures from cryo-EM density maps is a crucial step in cryo-EM-based protein structure determination. Converting density maps into 3D atomic structures for proteins lacking accurate homologous or predicted structures as templates remains a significant challenge. Here, we introduce Cryo2Struct, a fully automated de novo cryo-EM structure modeling method. Cryo2Struct utilizes a 3D transformer to identify atoms and amino acid types in cryo-EM density maps, followed by an innovative Hidden Markov Model (HMM) to connect predicted atoms and build protein backbone structures. Cryo2Struct produces substantially more accurate and complete protein structural models than the widely used ab initio method Phenix. Additionally, its performance in building atomic structural models is robust against changes in the resolution of density maps and the size of protein structures.

Volume density maps of the 862 nm DIB carrier and interstellar dust. Hints on the role of carbon-rich ejecta from AGB stars

The carbonaceous macromolecules imprinting the numerous absorptions called diffuse interstellar bands (DIBs) in astronomical spectra are omnipresent in the Galaxy and beyond. They represent a considerable reservoir of organic matter. However, their chemical formulae, formation, and destruction sites remain unknown. Their spatial distribution and the local relation to other interstellar species is key to tracing their role in the lifecycle of organic matter. Volume density maps bring local instead of line-of-sight distributed information and allow for new diagnostics to be captured. We present the first large-scale volume (3D) density map of a DIB carrier and compare it with an equivalent map of interstellar dust. The DIB carrier map was obtained through hierarchical inversion of sim 202,000 measurements of the 8621 nm DIB obtained with the Gaia -RVS instrument. It covers about 4000 pc around the Sun in the Galactic plane. We built a dedicated interstellar dust map based on the extinction towards the same target stars. At the simeq 50 pc resolution of the maps, the shape of the 3D DIB distribution is found to be remarkably similar to the 3D distribution of dust. On the other hand, the DIB-to-dust local density ratio increases in low-dust areas. It is also increasing away from the disk, however, the minimum ratio is found to be shifted above the Galactic plane to Z=simeq +50pc. Finally, the average ratio is also surprisingly found to increase away from the Galactic Center. We suggest that the three latter trends may be indications of a dominant contribution of material from the carbon-rich category of dying giant stars to the formation of the carriers. Our suggestion is based on recent catalogs of asymptotic giant branch (AGB) stars and estimates of the mass fluxes of their C-rich and O-rich ejecta.

AdaTreeFormer: Few shot domain adaptation for tree counting from a single high-resolution image

The process of estimating and counting tree density using only a single aerial or satellite image is a difficult task in the fields of photogrammetry and remote sensing. However, it plays a crucial role in the management of forests. The huge variety of trees in varied topography severely hinders tree counting models to perform well. The purpose of this paper is to propose a framework that is learnt from the source domain with sufficient labeled trees and is adapted to the target domain with only a limited number of labeled trees. Our method, termed as AdaTreeFormer, contains one shared encoder with a hierarchical feature extraction scheme to extract robust features from the source and target domains. It also consists of three subnets: two for extracting self-domain attention maps from source and target domains respectively and one for extracting cross-domain attention maps. For the latter, an attention-to-adapt mechanism is introduced to distill relevant information from different domains while generating tree density maps; a hierarchical cross-domain feature alignment scheme is proposed that progressively aligns the features from the source and target domains. We also adopt adversarial learning into the framework to further reduce the gap between source and target domains. Our AdaTreeFormer is evaluated on six designed domain adaptation tasks using three tree counting datasets, i.e. Jiangsu, Yosemite, and London. Experimental results show that AdaTreeFormer significantly surpasses the state of the art, e.g. in the cross domain from the Yosemite to Jiangsu dataset, it achieves a reduction of 15.9 points in terms of the absolute counting errors and an increase of 10.8% in the accuracy of the detected trees’ locations. The codes and datasets are available at https://github.com/HAAClassic/AdaTreeFormer.

Improved STNNet, A benchmark for detection, tracking, and counting crowds using Drones

In computer vision, navigating multi-object tracking in crowded scenes poses a fundamental challenge with broad applications ranging from surveillance systems to autonomous vehicles. Traditional tracking methods encounter difficulties associating noisy object detections and maintaining consistent labels across frames, particularly in scenarios like video surveillance for crowd control and public safety. This paper introduces ’Improved Space-Time Neighbor-Aware Network (STNNet),’ an advanced framework for online Multi-Object Tracking (MOT) designed to address these challenges. Expanding upon the foundational STNNet architecture, our enhanced model incorporates deep reinforcement learning techniques to refine decision-making. By framing the online MOT problem as a Markov Decision Process (MDP), Improved STNNet learns a sophisticated policy for data association, adeptly handling complexities such as object birth/death and appearance/disappearance as state transitions within the MDP. Through extensive experimentation on benchmark datasets, including the MOT Challenge, our proposed Improved STNNet demonstrates superior performance, surpassing existing methods in demanding, crowded scenarios. This study showcases the effectiveness of our approach and lays the groundwork for advancing real-time video analysis applications, particularly in dynamic, crowded environments. Additionally, we utilize the dataset provided by STNNET for density map estimation, forming the basis for our research.•Develop an advanced framework for online Multi-Object Tracking (MOT) to address crowded scene challenges, particularly improving object association and label consistency across frames.•Explore integrating Deep Reinforcement learning techniques into the MOT framework, framing the problem as an MDP to refine decision-making and handle complexities such as object birth or death and appearance or disappearance transitions.

SLCOBNet: Shrimp larvae counting network with overlapping splitting and Bayesian-DM-count loss

Estimating the number of shrimp larvae plays a critical role for achieving reasonable feeding in aquaculture. However, previous shrimp larvae counting models failed to accurately distinguish between shrimp larvae and other objects due to the loss of shrimp larvae continuity information. Also, shrimp larvae counting has the challenges of multi-scale changes, transparent body overlap, and background noise. To solve the above problems, a novel shrimp larvae counting network called SLCOBNet is proposed. First, overlapping splitting image is devised, with each patch sharing half of its area, ensuring the continuity of information regarding shrimp larvae between patches. Then, shrimp larvae feature of different scales and multi-scale density maps are obtained through the feature pyramid aggregation and the regression head with multi-scale receptive fields, respectively. Moreover, a novel loss function called Bayesian-DM-Count loss, composed of DM-Count loss and Bayesian loss, was designed to address the existing transparent body overlap and background noise problems. Experiments were performed by collecting shrimp larvae data from the breeding trays of a real aquaculture farm. The extensive experimental results on three shrimp larvae datasets have shown that SLCOBNet achieves 3.27, 3.61 and 1.28 in Mean Absolute Error. Hence, the proposed method exhibits a better counting accuracy compared to other counting methods. Furthermore, the predicted results were consistent with the true values.

MDFF_NM: Improved Molecular Dynamics Flexible Fitting into Cryo-EM Density Maps with a Multireplica Normal Mode-Based Search.

Molecular Dynamics Flexible Fitting (MDFF) is a widely used tool to refine high-resolution structures into cryo-EM density maps. Despite many successful applications, MDFF is still limited by its high computational cost, overfitting, accuracy, and performance issues due to entrapment within wrong local minima. Modern ensemble-based MDFF tools have generated promising results in the past decade. In line with these studies, we present MDFF_NM, a stochastic hybrid flexible fitting algorithm combining Normal Mode Analysis (NMA) and simulation-based flexible fitting. Initial tests reveal that, besides accelerating the fitting process, MDFF_NM increases the diversity of fitting routes leading to the target, uncovering ensembles of conformations in closer agreement with experimental data. The potential integration of MDFF_NM with other existing methods and integrative modeling pipelines is also discussed.

Automated multiconformer model building for X-ray crystallography and cryo-EM.

In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift toward modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior Rfree and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g., Coot) and fit can be further improved by refinement using standard pipelines (e.g., Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.

Automated multiconformer model building for X-ray crystallography and cryo-EM

In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift toward modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior Rfree and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g., Coot) and fit can be further improved by refinement using standard pipelines (e.g., Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.

All-sky three-dimensional dust density and extinction Maps of the Milky Way out to 2.8 kpc

ABSTRACT Three-dimensional dust density maps are crucial for understanding the structure of the interstellar medium of the Milky Way and the processes that shape it. However, constructing these maps requires large data sets and the methods used to analyse them are computationally expensive and difficult to scale up. As a result, it has only recently become possible to map kiloparsec-scale regions of our Galaxy at parsec-scale grid sampling. We present all-sky three-dimensional dust density and extinction maps of the Milky Way out to 2.8 kpc in distance from the Sun using the fast and scalable Gaussian Process algorithm Dustribution. The sampling of the three-dimensional map is l, b, d = 1° × 1° × 1.7 pc. The input extinction and distance catalogue contains 120 million stars with photometry and astrometry from Gaia DR2, 2MASS and AllWISE. This combines the strengths of optical and infrared data to probe deeper into the dusty regions of the Milky Way. We compare our maps with other published 3D dust maps. All maps quantitatively agree at the 0.001 mag pc−1 scale with many qualitatively similar features, although each map also has its own features. We recover Galactic features previously identified in the literature. Moreover, we also see a large under-density that may correspond to an inter-arm or -spur gap towards the Galactic Centre.

Spatial and temporal trends of Tamanend’s bottlenose dolphin (Tursiops erebennus) strandings in South Carolina, USA, 2006–2020

Marine mammal strandings data contribute towards overall assessments of cetacean populations, including seasonal, annual and life‐history trends which result from both natural and anthropogenic causes. We conducted spatial and temporal analyses of Tamanend’s bottlenose dolphin (Tursiops erebennus) strandings in the waters of South Carolina, USA, over a 15‐year period from 2006 to 2020, with the following objectives: (1) to determine spatial and temporal trends; (2) to analyse seasonal reproductive trends; (3) to determine life‐history parameters, such as sex ratio and age class; (4) to determine the extent to which human interaction contributed to strandings; and (5) to compare stranding patterns with historical data from 1992–2005. A total of 837 strandings occurred over the study period, with a mean of 55.8 strandings per annum. The season with most strandings was Spring (April–June), while March and April had the highest number of strandings. A relatively equal number of male and females dolphins stranded, and mortality was highest in neonates, first‐year calves and adults. Neonatal strandings comprised 22.1% of all strandings and were predominant in May. Ninety‐five human‐interaction (HI) cases were observed, representing 22.9% of strandings where HI or non‐HI could be determined. Confirmed crab‐pot buoy‐line entanglements were the predominant HI category (n = 31). Density maps and hot spot analysis showed most strandings, including live and neonatal strandings and HI cases, occurred in defined areas of Charleston and Beaufort Counties. While many trends were similar to historical data, some new trends emerged, particularly an increase of strandings in March and April. Neonatal strandings decreased in November and the historical spike during this month has essentially disappeared. The results of this analysis serve as a tool to predict stranding rates and inform conservation and management decisions to better protect bottlenose dolphins.

Petrographic and chemical characterization and carbon and nitrogen isotopic compositions of cometary IDPs and their GEMS amorphous silicates

GEMS (glass with embedded metal and sulfides) are the dominant carrier of amorphous silicates in anhydrous interplanetary dust particles (IDPs) and one of the most suitable materials to study early solar system processes. Amorphous silicates in 105 GEMS from eight IDPs were analyzed regarding texture and chemical composition to reassess GEMS formation theories and genetic relationships to amorphous silicate material in meteorites. Petrography of bulk IDPs was investigated to understand GEMS’ relationships to other IDP components. Furthermore, carbon and nitrogen isotopic compositions were measured. Nearly all GEMS are aggregates of several subgrains with variable amount of nanophase inclusions and different Mg- and Si-contents, while single GEMS are rare. The subgrains within aggregates are typically surrounded by one or more carbon rims with high density. The chemical compositions of GEMS amorphous silicates are subsolar for all major element/Si ratios but exhibit wide heterogeneity. This is not influenced by silicon oil from the capturing process of IDPs as assumed before, as a penetration of the silicon oil is excluded by high resolution EELS (electron energy loss spectroscopy) areal density maps of silicon. Furthermore, low Fe-content in GEMS amorphous silicates shows that these are not altered by aqueous activity on the parent body as it is the case for amorphous silicate material in primitive meteorites. The subsolar element/Si ratios and the wide chemical heterogeneity point to a non-equilibrium fractional condensation origin either in the early solar nebula or in a circumstellar environment and are not in agreement with homogenization via sputtering in the ISM. The close association with carbon around GEMS subgrains and as double-rims around GEMS aggregates argue for a multi-step aggregation after formation of the smallest GEMS subgrains in the ISM or the early solar nebula. Carbon acting as matrix material connecting GEMS and other IDP components has lower areal density as seen from carbon EELS areal density maps and isotopic anomalies varying at the nanometer scale, pointing to different origins and processing of materials to varying extent or at changing temperatures.To balance GEMS’ subsolar element/Si ratios, a supersolar component in IDPs was assumed to account for the overall chondritic composition of bulk IDPs. Nevertheless, our bulk IDP analyses revealed subsolar, but variable, element/Si ratios for complete particles as well, depending on type and amount of mineral phases in each particle. Pyroxenes in the investigated particles can occur as elongated euhedral crystals, but are overall rare. The dominant crystalline fraction in the investigated IDP samples are equilibrated aggregates (EAs) that show the same chemical compositions as GEMS, indicating that the EAs are recrystallized GEMS grains and formed after GEMS formation as secondary phases.

Multi-branch progressive embedding network for crowd counting

Crowd counting is essential for video surveillance and public safety. The performance of counting models has been greatly improved with the rapid development of Convolution neural networks (CNN), while it still suffers interference from complex background and large-scale variation. To relieve the above challenges, this paper proposes a novel Multi-branch Progressive Embedding Network (MPENet) for crowd counting. Specifically, the proposed network mainly consists of two modules named Background Area Filter (BAF) and Sequential Multi-scale Modules (SMM), which are embedded with each other to generate higher-quality density maps. Firstly, the BAF model base on attention mechanism is proposed to distinguish crowd from background, which effectively avoids the model outputting positive predictions in the background region. Meanwhile, a multi-level supervision mechanism is proposed to generate more accurate attention maps. Besides, the SMM module is designed to be progressively embedded with scale context information so that the scale feature will be smooth and continuous. Finally, a novel multi-scale consistency structural loss is proposed to avoid pixel-level isolation due to Euclidean loss. The proposed method significantly improves counting accuracy, achieving Mean Absolute Error (MAE) of 57.6 and 6.9 on ShanghaiTechA and ShanghaiTechB respectively.

Forest Fire Prediction: A Spatial Machine Learning and Neural Network Approach

The study of forest fire prediction holds significant environmental and scientific importance, particularly in regions like South Carolina (SC) with a high incidence rate of forest fires. Despite the limited existing research on forest fires in this area, the application of machine learning and neural network techniques presents an opportunity to enhance forest fire prevention and control efforts. Utilizing data of forest fire from the SC Forestry Commission for the year 2023, prediction models were developed incorporating various factors such as meteorology, terrain, vegetation, and infrastructure—key drivers of forest fires in SC. Feature importance analysis was employed to construct the final fire prediction model using different machine learning and neural network approaches including Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). Correlation coefficients analysis was employed to construct the final fire hazard map using a correlation test. The evaluation of predictive performance based on accuracy scores revealed that the DT model achieved the highest accuracy of 90.58%, surpassing other models. However, based on the kernel density map of the fire data from 2000 to 2023, the correlation test gave the better fire hazard map compared to any other machine learning or neural network approach that utilized feature importance. Nonetheless, all models achieved prediction accuracies exceeding 80%. This finding directed us to the approach based on the correlation coefficients rather than to those just based on feature importance. The overlap between fire locations and carbon hotspots provided the immediate need to mitigate the carbon loss due to fire in those locations. These results serve as a valuable resource for forest fire prediction in SC, demonstrating the efficacy of the correlation test, providing a theoretical foundation and data support for future forestry applications in the region, and showing the outperforming capability of this method compared to other approaches based on feature importance and the importance to prioritize areas to mitigate the climate change impact based upon fire prediction.

Insight into the molecular basis of the soluble storage-state to fibrous-state structural transformation of spider pyriform silk

Pyriform (or piriform) silk is a fibrous spider silk used in a composite material to attach web silks to substrates and disparate silk types together. Argiope argentata pyriform silk is formed from a protein with a central repetitive region consisting of a 234-amino acid unit (the "Py unit") repeated 21 times. We have previously shown that the Py unit behaves modularly, with a structured core of 6 α-helices that retains its structuring upon truncation of N- and C-terminal intrinsically disordered segments. Here, we apply solution-state nuclear magnetic resonance (NMR) spectroscopy to investigate the structured core of the Py unit. Based on 15N spin relaxation measurements and reduced spectral density mapping at two field strengths, the α-helical core of the Py unit exhibits minimal backbone motion beyond the tumbling of the globular domain except for one interhelical linker that exhibits a notably higher degree of dynamics. Evaluation of rotational diffusion based on 15N spin relaxation and translational diffusion by pulsed field gradient diffusion NMR demonstrates that the core of the Py unit behaves as a compact structure, with hydrodynamic behaviour consistent with a well-packed protein. Pyriform silk protein undergoes a structural transformation upon conversion from the soluble-state to the fibrous-state, with a loss of αhelical content and gain of β-sheet content. The more dynamic segment we observe midway through the structured core of the Py core, especially in the context of the intrinsically disordered segments that flank the core, would provide an initiation point for decompaction, structural transformation, and/or intermolecular interactions.

UVIT Study of the MAgellanic Clouds (U-SMAC) – I. Recent star formation history and kinematics of the Shell region in the north-eastern Small Magellanic Cloud

ABSTRACT The interactions between the Magellanic Clouds significantly affect the shape and distribution of the young stellar population, particularly in the periphery of the Small Magellanic Cloud (SMC). We present the first far-UV (FUV) map of the north-east SMC-Shell region using the Ultra Violet Imaging Telescope (UVIT) onboard AstroSat. The detected FUV stars are combined with Gaia Early Data Release 3 data to create a FUV–optical catalogue of ∼14 400 stars. FUV–optical colour-magnitude diagrams are used along with isochrones to estimate the stellar ages. The detected stars are formed in multiple episodes. We identified two episodes of star formation (∼60 and ∼260 Myr ago), where the episode at ∼260 Myr is linked to the recent interaction with the Large Magellanic Cloud (LMC) and the episode at ∼60 Myr is linked to the pericentric passage of the SMC around our Galaxy. The median proper motion (PM) and velocity dispersion are found to be similar to the SMC main body, indicating that this region has not experienced significant tidal effects. The FUV stellar surface density and the dispersion in PM suggest that the extent of the inner SMC in the north-east direction to be around 2.2°. We detect arm-like and arc-like structures in the FUV stellar density map, and their kinematics appear to be similar to the SMC main body. These extended outer features are the spatial stellar overdensities formed over multiple episodes of star formation, but without apparent kinematic distinction.

Visual analysis of fitness landscapes in architectural design optimization

In architectural design optimization, fitness landscapes are used to visualize design space parameters in relation to one or more objective functions for which they are being optimized. In our design study with domain experts, we developed a visual analytics framework for exploring and analyzing fitness landscapes spanning data, projection, and visualization layers. Within the data layer, we employ two surrogate models and three sampling strategies to efficiently generate a wide array of landscapes. On the projection layer, we use star coordinates and UMAP as two alternative methods for obtaining a 2D embedding of the design space. Our interactive user interface can visualize fitness landscapes as a continuous density map or a discrete glyph-based map. We investigate the influence of surrogate models and sampling strategies on the resulting fitness landscapes in a parameter study. Additionally, we present findings from a user study (N = 12), revealing how experts’ preferences regarding projection methods and visual representations may be influenced by their level of expertise, characteristics of the techniques, and the specific task at hand. Furthermore, we demonstrate the usability and usefulness of our framework by a case study from the architecture domain, involving one domain expert.

Cross-modal collaborative feature representation via Transformer-based multimodal mixers for RGB-T crowd counting

RGB-T crowd counting aims to utilize the thermal source data to compensate for the insufficient RGB feature representation under low-illumination conditions. However, how to efficiently use complementary information from different modalities is still a challenging issue for the target task. Existing CNN-based RGB-T crowd counting methods focus on the cross-modal feature representation via the local fixed-receptive-field convolutional operations and the transformer-based works mainly utilize the mutual attention, failing to realize the global cross-modal feature representation. To this end, this paper designs a multimodal counting network based on the multimodal transformer mixer to realize cross-modal collaborative feature representation. Firstly, we design a Transformer-based multimodal mixer to fully fuse the features from both modalities. And then we utilize the Transformer-based network with the multi-head self-attention layer replaced by the average pooling layer to build the two-stream backbone to extract rich multi-scale feature information of the two modalities. Meanwhile, the features extracted from the mixers are used to enhance the intermediate features to achieve the preservation of crowd detail information and suppression of background information. Finally, we design a pyramid regression head to aggregate the multi-scale feature maps to regress the density maps. Extensive experiments, as well as ablation studies, are conducted on challenging RGB-T crowd counting benchmark datasets (RGBT-CC and DroneRGBT), and the achieved competitive experimental results demonstrate the effectiveness of the proposed method on cross-modal collaborative feature representation for target task.