Explore the vast range of titles available.

An accelerating trend of global urbanization accompanying various environmental and urban issues makes frequently urban mapping. Nighttime light data (NTL) has shown great advantages in urban mapping at regional and global scales over long time series because of its appropriate spatial and temporal resolution, free access, and global coverage. However, the existing urban extent extraction methods based on nighttime light data rely on auxiliary data and training samples, which require labor and time for data preparation, leading to the difficulty to extract urban extent at a large scale. This study seeks to develop an unsupervised method to extract urban extent from nighttime light data rapidly and accurately without ancillary data. The clustering algorithm is applied to segment urban areas from the background and multi-scale spatial context constraints are utilized to reduce errors arising from the low brightness areas and increase detail information in urban edge district. Firstly, the urban edge district is detected using spatial context constrained clustering, and the NTL image is divided into urban interior district, urban edge district and non-urban interior district. Secondly, the urban edge pixels are classified by an adaptive direction filtering clustering. Finally, the full urban extent is obtained by merging the urban inner pixels and the urban pixels in urban edge district. The proposed method was validated using the urban extents of 25 Chinese cities, obtained by Landsat8 images and compared with two common methods, the local-optimized threshold method (LOT) and the integrated night light, normalized vegetation index, and surface temperature support vector machine classification method (INNL-SVM). The Kappa coefficient ranged from 0.687 to 0.829 with an average of 0.7686 (1.80% higher than LOT and 4.88% higher than INNL-SVM). The results in this study show that the proposed method is a reliable and efficient method for extracting urban extent with high accuracy and simple operation. These imply the significant potential for urban mapping and urban expansion research at regional and global scales automatically and accurately.

The generative modeling of 3D objects in the real world is an interesting but challenging task commonly constrained by process and order. Most existing methods focus on spatial relations to address this issue, neglecting the rich information between temporal sequences. To close this gap, we deliver a spatial-temporal context-aware network to explore the prediction of ordered actions for 3D object construction. Specifically, our approach is mainly formed by two modules, i.e., the spatial-context module and the temporal-context module. The spatial-context module is designed to learn the physical constraints in 3D object construction, such as spatial constraints and gravity. Meanwhile, the temporal-context module integrates the temporal context of action orders in history on the fly toward more accurate predictions. After that, the features of such two modules are merged to finalize the perdition of the following action’s position and block type. The entire model is optimized by the stochastic gradient descent optimization (SGD) method in an end-to-end manner. Extensive experiments conducted on the 3D-Craft dataset demonstrate that the proposed method surpasses the state-of-the-art methods with a large margin, i.e., improving 4.5 % absolute ACC@1, 3.3 % absolute ACC@5, and 4.1 % absolute ACC@10. Moreover, the comprehensive ablation studies and insightful analysis further validate the effectiveness of the proposed method.

Introduction Retrieval practice has been shown to be an effective means of learning new information, a memory phenomenon known as the testing effect or the retrieval practice effect. Some work suggests that the magnitude of the testing effect can be enhanced when the test used for retrieval practice uses fewer cues to retrieve previously studied information. It is unclear, however, whether such testing benefits extend to peripheral contextual details associated with studied materials (e.g., location where stimuli appear, font color in which items are presented, etc.). In this experiment, we examine both item memory (i.e., memory for the studied items) and context memory under conditions where the intervening test offers fewer cues (i.e., lower constraint) compared to more cues (higher constraint) to better understand item and context memory testing effects. Methods Participants first studied word pairs presented in one of eight locations as well as in either red or green font color. Then, in the re‐exposure phase, participants processed materials in two types of intervening tests (lower constraint and a higher constraint test) as well as in a restudy condition, before a final memory test. Results For item memory, results showed that memory was better in the lower constraint testing condition compared to both the higher constraint testing condition as well as the restudy (control) condition. For context memory, results indicated improved memory for location context under lower constraint testing compared to both higher constraint testing and restudy conditions. There was no difference in memory, however, for color context across all conditions. Conclusion Overall, these findings suggest that providing fewer cues to aid retrieval in the intervening test can induce better memory for both items as well as some contextual details. Research has shown that testing is an effective strategy for improving memory for previously learned material compared to restudying (i.e., the testing effect or the retrieval practice effect). In this investigation, we examine whether providing less information about target items during a practice test (a lower constraint test) improves item and context memory over a practice test with more information about target items (a higher constraint test). Results showed that the lower constraint practice test led to better memory relative to the higher constraint test, restudy, and control conditions. Overall, we establish a way to increase the magnitude of the testing effect in memory, which may be a way to optimize memory using this well‐known study strategy.

The segmentation of remote sensing images has emerged as a compelling undertaking in computer vision owing to its use in the development of several applications. The U-Net style has been extensively utilized in many picture segmentation applications, yielding remarkable achievements. Nevertheless, the U-Net has several constraints in the context of remote sensing picture segmentation, mostly stemming from the limited scope of the convolution kernels. The transformer is a deep learning model specifically developed for sequence-to-sequence translation. It incorporates a self-attention mechanism to efficiently process many inputs, selectively retaining the relevant information and discarding the irrelevant inputs by adjusting the weights. However, it highlights a constraint in the localization capability caused by the absence of fundamental characteristics. This work presents a novel approach called U-Net–transformer, which combines the U-Net and transformer models for the purpose of remote sensing picture segmentation. The suggested solution surpasses individual models, such as U-Net and transformers, by combining and leveraging their characteristics. Initially, the transformer obtains the overall context by encoding tokenized picture patches derived from the feature maps of the convolutional neural network (CNN). Next, the encoded feature maps undergo upsampling through a decoder and are then merged with the high-resolution feature maps of the CNN model. This enables the localization to be more accurate. The transformer serves as an unconventional encoder for segmenting remote sensing images. It enhances the U-Net model by capturing localized spatial data, hence improving the capacity to capture intricate details. The U-Net–transformer, as suggested, has demonstrated exceptional performance in remote sensing picture segmentation across many benchmark datasets. The given findings demonstrated the efficacy of integrating the U-Net and transformer model for the purpose of segmenting remote sensing images.

Human Pose Estimation (HPE) remains challenging due to scale variation, occlusion, and high computational costs. Standard methods often struggle to capture detailed spatial information when keypoints are obscured, and they typically rely on computationally expensive deconvolution layers for upsampling, making them inefficient for real-time or resource-constrained scenarios. We propose AMFACPose (Attentive Multi-scale Features with Adaptive Context PoseResNet) to address these limitations. Specifically, our architecture incorporates Coordinate Convolution 2D (CoordConv2d) to retain explicit spatial context, alleviating the loss of coordinate information in conventional convolutions. To reduce computational overhead while maintaining accuracy, we utilize Depthwise Separable Convolutions (DSCs), separating spatial and pointwise operations. At the core of our approach is an Adaptive Feature Pyramid Network (AFPN), which replaces costly deconvolution-based upsampling by efficiently aggregating multi-scale features to handle diverse human poses and body sizes. We further introduce Dual-Gate Context Blocks (DGCBs) that refine global context to manage partial occlusions and cluttered backgrounds. The model integrates Squeeze-and-Excitation (SE) blocks and the Spatial–Channel Refinement Module (SCRM) to emphasize the most informative feature channels and spatial regions, which is particularly beneficial for occluded or overlapping keypoints. For precise keypoint localization, we replace dense heatmap predictions with coordinate classification using Multi-Layer Perceptron (MLP) heads. Experiments on the COCO and CrowdPose datasets demonstrate that AMFACPose surpasses the existing 2D HPE methods in both accuracy and computational efficiency. Moreover, our implementation on edge devices achieves real-time performance while preserving high accuracy, confirming the suitability of AMFACPose for resource-constrained pose estimation in both benchmark and real-world environments.

It has been persuasively argued that relationship networks affect the socio-economic behaviors of actors. However, few studies have recognized the location and context of actors in relationship network. To address this challenge, this paper examined the skill learning and chain migration which were affected by relationship network within spatial situation, by using data covering 115 households in the specialized village of fried dough sticks (youtiao). The results showed learning from neighbors with geographical closeness played an important role in expanding the space and enhancing efficiency of skill learning. It could be noted that the establishment of master-prentice relationship networks was related to the spatial proximity of farmers’ dwellings, and constrained by the space of villagers’ group. Farmers’ chain migration showed the closer the spatial distance of farmers, the nearer the migration destination they choose. Farmers’ livelihoods were constrained by the differences of spatial contexts. Farmers with smaller amounts of cultivated land were more likely to flow into cities with long distance for selling fried dough sticks, and they usually became fixed merchants. In contrast, farmers with more cultivated land were more likely to migrate to the countryside with short distance and usually became mobile vendors. It should better understand the socio-economic behaviors and the change of regional livelihoods, if we will focus on relationship networks embedded in spatial situation in future research.

Intelligent geoprocessing relies heavily on formalized parameter constraints of geoprocessing tools to validate the input data and to further ensure the robustness and reliability of geoprocessing. However, existing methods developed to formalize parameter constraints are either designed based on ill-suited assumptions, which may not correctly identify the invalid parameter inputs situation, or are inefficient to use. This paper proposes a novel method to formalize the parameter constraints of geoprocessing tools, based on a high-level and standard constraint language (i.e., SHACL) and geoprocessing ontologies, under the guidance of a systematic classification of parameter constraints. An application case and a heuristic evaluation were conducted to demonstrate and evaluate the effectiveness and usability of the proposed method. The results show that the proposed method is not only comparatively easier and more efficient than existing methods but also covers more types of parameter constraints, for example, the application-context-matching constraints that have been ignored by existing methods.

We present a lightweight and efficient semi-supervised video object segmentation network based on the space-time memory framework. To some extent, our method solves the two difficulties encountered in traditional video object segmentation: one is that the single frame calculation time is too long, and the other is that the current frame’s segmentation should use more information from past frames. The algorithm uses a global context (GC) module to achieve high-performance, real-time segmentation. The GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real time. Moreover, the prediction mask of the previous frame is helpful for the segmentation of the current frame, so we input it into a spatial constraint module (SCM), which constrains the areas of segments in the current frame. The SCM effectively alleviates mismatching of similar targets yet consumes few additional resources. We added a refinement module to the decoder to improve boundary segmentation. Our model achieves state-of-the-art results on various datasets, scoring 80.1% on YouTube-VOS 2018 and a J & F score of 78.0% on DAVIS 2017, while taking 0.05 s per frame on the DAVIS 2016 validation dataset.

Recent experience with the design of bus services in Santiago, Chile, seems to confirm Jansson's (1980) assertion regarding observed planned bus frequency and size being too low and too large, respectively. We offer an explanation based upon the relation between cost coverage, pricing and optimal design variables. We recall that average social cost decreases with patronage, which generates an optimal monetary fare below the average operators' cost, inducing an optimal subsidy. Then we compare optimal frequency and bus size—those that minimize total social costs—with those that minimize operators' costs only. We show that an active constraint on operators' expenses is equivalent to diminish the value of users' time in the optimal design problem. Inserting this property back in the optimal pricing scheme, we conclude that a self-financial constraint, if active, always provokes an inferior solution, a smaller frequency and, under some circumstances, larger than optimal buses.

Avec l'apparition de la pandémie de maladie a coronavirus 2019 (COVID-19), des mesures de santé publique telles que la distanciation physique ont été mises en place afin de limiter la transmission du virus a l'origine de la maladie. Néanmoins, adopter la méme approche dans toutes les régions sans tenir compte du contexte pourrait réduire l'efficacité de ces mesures et avoir des conséquences négatives imprévues, comme la perte des moyens de subsistance et l'insécurité alimentaire. Avant de planifier et de déployer des mesures utiles et adaptées a la situation en vue de ralentir la transmission au sein des communautés, il est impératif d'identifier les contraintes liées notamment aux lieux oü la distanciation physique est impossible a respecter. Le présent document se concentre sur l'Afrique subsaharienne. Nous y avons présenté et évoqué les défis auxquels sont confrontés les habitants des implantations urbaines sauvages au cours de l'actuelle pandémie de COVID-19. Nous décrivons comment intégrer les nouveaux ensembles de données géospatiales pour obtenir des informations plus détaillées sur les contraintes locales liées a la distanciation physique et trouver des solutions alternatives permettant de limiter la transmission de la COVID-19 d'une personne a l'autre. Nous citons une étude de cas réalisée dans le comté de Nairobi, au Kenya, dont les résultats cartographiés illustrent les variations intra-urbaines qui déterminent la faisabilité de la distanciation physique et les difficultés que les habitants de nombreuses implantations sauvages sont susceptibles de rencontrer. Nos exemples révelent le potentiel des nouveaux ensembles de données géospatiales dans (analyse et l'élaboration des politiques et mesures de santé publique, y compris pour la COVID-19.