Boundary Map Research Articles

Hilbert reciprocity using $K$-theory localization

Usually the boundary map in K-theory localization only gives the tame symbol at $K_{2}$. It sees the tamely ramified part of the Hilbert symbol, but no wild ramification. Gillet has shown how to prove Weil reciprocity using such boundary maps. This implies Hilbert reciprocity for curves over finite fields. However, phrasing Hilbert reciprocity for number fields in a similar way fails because it crucially hinges on wild ramification effects. We resolve this issue, except at p=2. Our idea is to pinch singularities near the ramification locus. This fattens up K-theory and makes the wild symbol visible as a boundary map.

New high resolution aeromagnetic anomaly map of Türkiye and its various derivative-based maps

Since Türkiye's territory is mostly mountainous and forested, earth science studies in these areas have not been completed at an adequate level and with a satisfying resolution. However, due to the long time and cost of collecting measurements from land in large- and medium-scale studies and due to the inability to collect measurements in regular grid intervals in large-scale studies to be carried out on land, an airborne geophysical survey project was conducted in order to collect high-resolution magnetic and radiometric country-scale data. Total magnetic field and radiometry data were collected from the air between 2017 and 2022 within the scope of the country-wide Airborne Geophysics Surveys Project. The data collection flights were carried out in such a way as to follow the topography from an altitude of about 200 m above the ground. The flights were carried out considering the regular geometry of the flight profile and the general geological and tectonic structure of Türkiye in the direction of N-S with one-kilometer profile intervals. A total field magnetic anomaly map was obtained after applying the diurnal, tie line leveling, IGRF (model 2020), and differential reduction to magnetic pole (DRTP) corrections to the magnetic data. For the scope of this study, the total field magnetic anomaly and the regional total field magnetic anomaly maps were presented along with the structural boundary maps (tilt angle (TA), the total horizontal derivative (THDR) and the analytical signal (AS)) derived from these maps. During another stage of the study, the general tectonic structure of Türkiye and structural boundaries obtained from derivative-based maps (AS, TA, and THDR) were compared. General geophysical interpretation was also performed with magnetic-anomaly and derivative-based maps for the three regions characterized by high magnetic anomalies.

A Deep Learning Approach to Mesh Segmentation

In the shape analysis community, decomposing a 3D shape into meaningful parts has become a topic of interest. 3D model segmentation is largely used in tasks such as shape deformation, shape partial matching, skeleton extraction, shape correspondence, shape annotation and texture mapping. Numerous approaches have attempted to provide better segmentation solutions; however, the majority of the previous techniques used handcrafted features, which are usually focused on a particular attribute of 3D objects and so are difficult to generalize. In this paper, we propose a three-stage approach for using Multi-view recurrent neural network to automatically segment a 3D shape into visually meaningful sub-meshes. The first stage involves normalizing and scaling a 3D model to fit within the unit sphere and rendering the object into different views. Contrasting viewpoints, on the other hand, might not have been associated, and a 3D region could correlate into totally distinct outcomes depending on the viewpoint. To address this, we ran each view through (shared weights) CNN and Bolster block in order to create a probability boundary map. The Bolster block simulates the area relationships between different views, which helps to improve and refine the data. In stage two, the feature maps generated in the previous step are correlated using a Recurrent Neural network to obtain compatible fine detail responses for each view. Finally, a layer that is fully connected is used to return coherent edges, which are then back project to 3D objects to produce the final segmentation. Experiments on the Princeton Segmentation Benchmark dataset show that our proposed method is effective for mesh segmentation tasks.

Automatic ROI Setting Method Based on LSC for a Traffic Congestion Area

Congested regions in videos put forward higher requirements for target detection algorithms, and the key detection of congested regions provides optimization directions for improving the accuracy of detection algorithms. In order to make the target detection algorithm pay more attention to the congested area, an automatic selection method of a traffic congestion area based on surveillance videos is proposed. Firstly, the image is segmented with superpixels, and a superpixel boundary map is extracted. Then, the mean filtering method is used to process the superpixel boundary map, and a fixed threshold is used to filter pixels with high texture complexity. Finally, a maximin method is used to extract the traffic congestion area. Monitoring data of night and rainy days were collected to expand the UA-DETRAC data set, and experiments were carried out on the extended data set. The results show that the proposed method can realize automatic setting of the congestion area under various weather conditions, such as full light, night and rainy days.

Study of Yeh Embang Watershed Characteristics for Sustainable Water Management

One of Bali’s perennial watershed that is experiencing the effects of climate change is the Yeh Embang Watershed. If it is not managed holistically, the sustainability of the current water availability will be impacted. This research will analyse the Yeh Embang Watershed’s characteristics in order to evaluate the watershed’s condition and decide how to manage the sustainability of the water resources. For this research, topographic information, boundary maps of the Yeh Embang watershed, maps of river network, maps of land use, and maps of soil types were all used. The appropriate sustainable management for Yeh Embang Watershed should be determined based on the characteristic. According to the characteristics of the Yeh Embang Watershed, integrated management from upstream to downstream be necessary for the watershed to be managed sustainably. In the upstream area, it is hoped that the forest will be protected by forest conservation to reduce the risk of erosion that could lead to flash floods, and the downstream portion of the Yeh Embang watershed will be structured to prevent flooding from harming the surrounding area. Infiltration wells can be used as a water-saving measure in residential areas in the Yeh Embang watershed to increase the amount of water that is absorbed and lessen surface runoff.

Performance limit of gas compression processes enhanced by self-utilization of compression waste heat

Self-utilization of compression waste heat has been proposed for the energy saving of gas compression process, in which the low-grade compression waste heat is recovered to generate cooling capacity in situ to cool the inlet gases of each stage compressor through thermally-driven refrigeration cycles. Efficiency enhancement of the gas compression process is achieved, since the actual compression process in this manner gets closer to an ideal isothermal compression process. However, the performance limits remain unclear due to the lack of consensus on internal coupling mechanism in the literature. Based on the theory of gas compression and thermo-power conversion, a thermodynamic model under ideal conditions is established by sequential Carnot cycles to analyze the performance limit of enhanced gas compression processes for a 60,000-Nm3/h scale air separation unit. The effects of key factors including cooling capacity distribution, number of compression stages, and ambient temperature on the energy conservation are further analyzed. The results show that when the ambient temperature is 25 ℃, the ideal energy saving rate of the three-stage self-enhancement system can reach 7.4%. The boundary maps of the performance improvement of the gas compression systems are given for reference in practice. In addition, a parametric analysis is carried out extending to the general compression systems. The results show that the single-stage pressure ratio, adiabatic index and isentropic efficiency of compressors, which directly affect the grade of waste heat, are the most critical factors affecting the performance of the self-enhancement system, while the distribution of cooling capacity has little effect. This study proves the superiority of this emerging improvement measure over traditional exothermic compression and multistage compression with effective interstage cooling, along with guidance for estimating energy saving potential under different conditions and in different applications.

Arahan Perubahan Penggunaan Lahan Berbasis Rendah Emisi Karbon Di Hulu Das Jeneberang

Jeneberang watershed is one of the watersheds in the MAMMINASATA area (Maros, Makassar, Sungguminasa and Takalar). The watershed, upstream, is now in a critical condition. This study aims to identify and map land use patterns, carbon stock emissions, both in the present condition and after rehabilitation condition. The data and information used in this research are Landsat 7 ETM+ in 2006 and 2010, the Jeneberang watershed boundary map, critical land map, forest area map, rainfall data, population data, and land capability class map. The method used is the interpretation of land use or land cover, biomass calculation, and analysis with REDD ABACUS. The results showed that land use was dominated by plantations and then followed by low density forest, agricultural dry land, rice fields, water bodies, plantation forests, vacant land, shrubs, settlements, high density forests, and savannas. The direction of land cover/land use that can increase carbon sequestration is the conversion of barren land, shrubs and savannas into plantation forests and agroforestry. Potential carbon stocks for plantations, dry land agriculture and rice fields will increase to 2.295.626,32 tons in 2026.

Gully Erosion Monitoring Based on Semi-Supervised Semantic Segmentation with Boundary-Guided Pseudo-Label Generation Strategy and Adaptive Loss Function

Gully erosion is a major threat to ecosystems, potentially leading to desertification, land degradation, and crop loss. Developing viable gully erosion prevention and remediation strategies requires regular monitoring of the gullies. Nevertheless, it is highly challenging to automatically access the monitoring results of the gullies from the latest monitoring data by training historical data acquired by different sensors at different times. To this end, this paper presents a novel semi-supervised semantic segmentation with boundary-guided pseudo-label generation strategy and adaptive loss function method. This method takes full advantage of the historical data with labels and the latest monitoring data without labels to obtain the latest monitoring results of the gullies. The boundary-guided pseudo-label generation strategy (BPGS), guided by the inherent boundary maps of real geographic objects, fuses multiple evidence data to generate reliable pseudo-labels. Additionally, we propose an adaptive loss function based on centroid similarity (CSIM) to further alleviate the impact of pseudo-label noise. To verify the proposed method, two datasets for gully erosion monitoring are constructed according to the satellite data acquired in northeastern China. Extensive experiments demonstrate that the proposed method is more appropriate for automatic gully erosion monitoring than four state-of-the-art methods, including supervised methods and semi-supervised methods.

Weakly supervised skin lesion segmentation based on spot‐seeds guided optimal regions

Automatic skin lesion segmentation is the most critical and relevant task in computer-aided skin cancer diagnosis. Methods based on convolutional neural networks (CNNs) are mainly used in current skin lesion segmentation. The requirement of huge pixel-level labels is a significant obstacle to achieve semantic segmentation of skin lesion by CNNs. In this paper, a novel weakly supervised framework for skin lesion segmentation is presented, which generates high-quality pixel-level annotations and optimizes the segmentation network. A hierarchical image segmentation algorithm can predict a boundary map for training images. Then, the optimal regions of candidate hierarchical levels are selected. Afterward, Superpixels-CRF built on the optimal regions is guided by spot seeds to propagate information from spot seeds to unlabeled regions, resulting in high-quality pixel-level annotations. Using these high-quality pixel-level annotations, a segmentation network can be trained and segmentation masks can be predicted. To iteratively optimize the segmentation network, the predicted segmentation masks are refined and the segmentation network are retrained. Comparative experiments demonstrate that the proposed segmentation framework reduces the gap between weakly and fully supervised skin lesion segmentation methods, and achieves state-of-the-art performance while reducing human labeling efforts.

Inter-District Road Infrastructure and Spatial Inequality in Rural Indonesia

Road quality plays an important role, especially in rural areas where most poor households are situated. This study aims to calculate the Rural Access Index (RAI), an indicator of rural road quality (SDG indicator 9.1.1), at the district level, to evaluate the implementation of the Nawacita programme in Indonesia from 2014–2020. The RAI describes the proportion of rural residents who live within 2 km of an all-season road. This study recommends the utilisation of road network maps, urban–rural boundary maps, three road network condition datasets, and WorldPop data to calculate the RAI. The results show that during this period, the RAI increased and its inequality decreased, specifically in the regions of priority for this programme (Papua and West Papua). The results also capture a strong pattern of regional convergence. To ensure the future success of this implementation, the government can create regulations to designate several road infrastructure projects as a national strategy, as well as increase tax collection and private sector investment as sources of road infrastructure development funding.

Beyond Crack: Fine-Grained Pavement Defect Segmentation Using Three-Stream Neural Networks

Pavement defect segmentation is a fundamental task in the field of transport infrastructure inspection. Existing methods mainly focus on detection/segmentation for long and thin cracks. However, there are many other types of defects with various sizes and shapes that are also essential to segment, which brings more challenges toward detailed road inspection. To address the above problems and provide a more comprehensive understanding of the overall road conditions, we propose a three-stream neural network that combines spatial, contextual and boundary information for fine-grained defect segmentation. Specifically, the spatial stream captures rich low-level spatial features. The contextual stream utilizes an attention mechanism and models high-level contextual relationships over local features. To further refine the segmentation results, the boundary stream encodes detailed boundaries using a global gated convolution and generates additional boundary maps. By combining the above different information, our model can effectively produce pixel-wise predictions for fine-grained road inspection. The network is trained using a dual-task loss in an end-to-end manner, and experiments were performed on three newly collected datasets, i.e., a fine-grained defect dataset and two crack datasets, which shows that the proposed method achieves favorable segmentation results on complex multi-class defects, and is also able to segment single-class cracks. Specifically, on the fine-grained dataset, it achieved state-of-the-art performance over other competing baselines (mPA of 0.54, mIoU of 0.38, Mic_F of 0.78 and Mac_F of 0.65), where each image is resized to 512 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 512$ </tex-math></inline-formula> and the processing speed is 21 FPS on average.

TransText: Improving scene text detection via transformer

Arbitrary shape scene text detection is still a challenging task in the computer vision community. Compared with anchor-based methods, segmentation-based methods have advantages in some respects, which detect text instances by generating accurate text components. In this paper, we propose a novel text components extraction network for arbitrary shape scene text detection. It can detect different text components through two parallel branches. In the first branch, the feature redistribution module (FRM) is proposed to extract text-related features and filter non-text information. Then, these text-related features are aggregated to generate text boundary maps. These text-related features are also fed to the second branch to progressively generate the text kernel maps. The second branch consists of a modified Transformer decoder with a multi-level progressive supervision strategy. It aims to capture spatial details and establish the dependencies between different text regions. In this way, our method can generate accurate text components without additional geometric calculations. Thanks to the powerful Transformer decoder and efficient differentiable binarization module, our method not only achieves advanced detection accuracy but also has a competitive inference speed. Specifically, with the ResNet-18 backbone, our method can run at 43.5 FPS and achieve an F-measure of 83.3% on the Total-Text dataset, 1.43 times faster than the latest state-of-the-art method. With the ResNet-50 backbone, our method achieves an F-measure of 85.2% and outperforms the latest state-of-the-art method by 0.2% on the Total-Text dataset. Code is available at: https://github.com/As-David/TransText.

Cataclysms for Anosov representations

In this paper, we construct cataclysm deformations for theta -Anosov representations into a semisimple non-compact connected real Lie group G with finite center, where theta subset Delta is a subset of the simple roots that is invariant under the opposition involution. These generalize Thurston’s cataclysms on Teichmüller space and Dreyer’s cataclysms for Borel-Anosov representations into mathrm {PSL}(n, mathbb {R}). We express the deformation also in terms of the boundary map. Furthermore, we show that cataclysm deformations are additive and behave well with respect to composing a representation with a group homomorphism. Finally, we show that the deformation is injective for Hitchin representations, but not in general for theta -Anosov representations.

Representation of the fermionic boundary operator

The boundary operator is a linear operator that acts on a collection of high-dimensional binary points (simplices) and maps them to their boundaries. This boundary map is one of the key components in numerous applications, including differential equations, machine learning, computational geometry, machine vision and control systems. We consider the problem of representing the full boundary operator on a quantum computer. We first prove that the boundary operator has a special structure in the form of a complete sum of fermionic creation and annihilation operators. We then use the fact that these operators pairwise anticommute to produce an $O(n)$-depth circuit that exactly implements the boundary operator without any Trotterization or Taylor series approximation errors. Having fewer errors reduces the number of shots required to obtain desired accuracies.

Forecasting future climate boundary maps (2021–2060) using exponential smoothing method and GIS

Future-oriented forecasts have an important place in making forward-looking decisions and planning. At the beginning of these studies is the monitoring and detection of climate change. The climate is very variable. Therefore, by making predictions about the climate, preliminary information about how and to what extent the climate will change can be obtained, and accordingly, necessary precautions can be taken quickly. This study aims to produce predictive climate boundary maps using Geographic Information Systems (GIS), in which climate classification methods and time series methods are evaluated to monitor and determine the changes caused by the climate in 13 selected provinces in Turkey. The triple exponential smoothing method and the Erinc climate classification method were discussed. The data were obtained from the General Directorate of Meteorology (GDM) between 1930 and 2020, and each year's precipitation efficiency index (Im) of the Erinc climate classification method was calculated. It is divided into two classes for forecasting and testing current indices: test Im indices (1930–2014) and forecast test Im indices (2015–2020). MAD, MSE, and MAPE criteria were calculated to determine whether the Im estimates were meaningful. However, the accuracy of the estimates was ensured by considering the MAPE criteria for this study. After this stage, the analyses were performed again with test Im indices (1930–2020) and forecast Im indices (2021–2060), and Im indices predictions for the future were made. Finally, the obtained forecast indices were subjected to GIS interpolation analyses (Kriging and IDW), and future climate boundary maps were produced. Thanks to the outputs obtained from the study, how the climate classes of any region will be in the future and to what extent they will change will be provided by evaluating the climate classification and time series methods together. It will contribute to different studies in this field with its innovative analysis approach.

A Research Paper on Lung Field Segmentation Techniques using Digital Image Processing

In this review study, we investigate various strategies for lung field segmentation by utilizing digital image processing. In chest radiographs (CXRs), lung field dissection segmentation is and will continue to be a crucial phase in the process of automatically evaluating pictures of this kind. We describe a method for the segmentation of the lung field that is based on a boundary map of high quality that was detected using a structured edge detector, which is a contemporary border detector (SED). A SED has previously been trained to recognize lung limits by using manually delineated lung sections in CXRs as training data. Following this step, the masked and tagged boundary map is converted into the active contour map (ACM). In conclusion, the lung contours that are created by following filter phases that are based on Gaussian and dilate features are the contours that have the highest rate of trust in the ACM. Our method is evaluated using aberrant lung pictures obtained from chest x-rays, and it is demonstrated to be superior, in terms of the amount of processing time required, to segmentation utilizing a universal contour map.

From Borderlands to Boundary Lines: Mapping on the Edges of the Ottoman Empire

ABSTRACT The evolution, nature and characteristics of boundary mapping in Central and Eastern Europe were distinctive, especially considering the imperial borders of states neighbouring the Ottoman Empire. Affected by strong military and geopolitical needs, demarcation maps were not based on cadastres or property mapping undertaken by civil authorities but exclusively on military mapping. The maps were a part of peace treaties with the Ottoman Empire, which gave them the power of legal documents. Moreover, boundary maps constituted an important tool in defending state sovereignty and subsequently in the state- and nation-building processes. In this article I analyse how countries bordering the Ottoman Empire made their demarcation maps, which strategies they applied, what level of cooperation they had with Ottoman cartographers, what cartographic conventions they met, and how these maps were received at the Ottoman court. I argue that boundary mapping was the key driver of territorial statehood in the region.

Penetapan Batas Nagari Berdasarkan Aspek Teknis dan Non-Teknis (Studi Kasus: Nagari Pasir Binjai, Kecamatan Silaut, Kabupaten Pesisir Selatan, Sumatera Barat)

The affirmation of village boundaries is the process of determining the coordinates of the boundaries, which can be carried out using cartometric methods and surveys in the field. It is outlined as a boundary map for village boundaries coordinate points as agreed. The determination of affirmation generally consists of three stages: collecting and researching boundary documents, determining a base map, and making a cartometric map of village boundaries. This activity aimed to determine boundary segments, objects, cartometric points, and artificial pillars for determining and confirming the boundaries of Pasir Binjai Village, Silaut District, and Pesisir Selatan Regency. The results of this activity were 1) Pasir Binjai Village has five boundary segments, all segments bordering the border villages within one administrative area of ​​the Silaut District government; 2) Natural boundaries, namely mark village boundary objects: river flow or middle of the river, middle of drainage or ditch, a centre of the road, and some are found on community plantation land; 3) the total number of cartometric points as a reference and the village boundary nodes are 15 points; 4) the number of pillars made for the artificial marking of the village boundaries are six pillars, there are four boundary pillars which are the primary boundary reference pillars which are not located right on the boundary line, and two boundary pillars are the main boundary pillars which are located right on the boundary line.

Edge-Aware Guidance Fusion Network for RGB–Thermal Scene Parsing

RGB–thermal scene parsing has recently attracted increasing research interest in the field of computer vision. However, most existing methods fail to perform good boundary extraction for prediction maps and cannot fully use high-level features. In addition, these methods simply fuse the features from RGB and thermal modalities but are unable to obtain comprehensive fused features. To address these problems, we propose an edge-aware guidance fusion network (EGFNet) for RGB–thermal scene parsing. First, we introduce a prior edge map generated using the RGB and thermal images to capture detailed information in the prediction map and then embed the prior edge information in the feature maps. To effectively fuse the RGB and thermal information, we propose a multimodal fusion module that guarantees adequate cross-modal fusion. Considering the importance of high-level semantic information, we propose a global information module and a semantic information module to extract rich semantic information from the high-level features. For decoding, we use simple elementwise addition for cascaded feature fusion. Finally, to improve the parsing accuracy, we apply multitask deep supervision to the semantic and boundary maps. Extensive experiments were performed on benchmark datasets to demonstrate the effectiveness of the proposed EGFNet and its superior performance compared with state-of-the-art methods. The code and results can be found at https://github.com/ShaohuaDong2021/EGFNet.

Pemanfaaran Teknologi Drone Untuk Pemetaan Batas Dan Potensi Desa, Desa Mongolato Kecamatan Telaga

The village is the smallest unit of government so that the development of a region or country currently focuses on developing the village economically and with resources. The boundaries of the Village area stated in the form of a Village Map are determined by a Regent/Mayor regulation. The purpose of this service is to create a tentative map of village boundaries and the potential of the detailed scale Mongolato village by utilizing drone technology. The method used is the remote sensing method with drone technology. The results obtained are that the village government has a village boundary map, a hamlet boundary map and a map of land use potential with a detailed scale. Based on the results obtained, it is hoped that it can provide recommendations to the district and provincial governments to make village maps with detailed scales throughout Gorontalo Province.