Competitive multiobjective multitask optimization (CMO-MTO) problems involve multiple tasks with comparable objectives but heterogeneous decision variables. The final Pareto front in CMO-MTO consists of multiple subse...
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The Ramsar Convention on Wetlands is an international framework through which countries identify and protect important *** Ramsar wetlands are under substantial anthropogenic pressure worldwide,and tracking ecological...
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The Ramsar Convention on Wetlands is an international framework through which countries identify and protect important *** Ramsar wetlands are under substantial anthropogenic pressure worldwide,and tracking ecological change relies on multitemporal data ***,we evaluated the spatial extent,temporal change,and anthropogenic threat to Ramsar wetlands at a national scale across China to determine whether their management is currently *** analyzed Landsat data to examine wetland dynamics and anthropogenic threats at the 57 Ramsar wetlands in China between 1980 and *** reveal that Ramsar sites play important roles in preventing wetland loss compared to the dramatic decline of wetlands in the surrounding ***,there are declines in wetland area at 18 Ramsar *** those,six lost a wetland area greater than 100 km^(2),primarily caused by agricultural *** expansion of anthropogenic land covers occurred within 43(75%)Ramsar sites,and anthropogenic threats from land cover change were particularly notable in eastern *** pond expansion and Spartina alterniflora invasion were prominent threats to coastal Ramsar *** observations within China’s Ramsar sites,which in management regulations have higher levels of protection than other wetlands,can help track progress towards achieving United Nations Sustainable Development Goals(SDGs).The study findings suggest that further and timely actions are required to control the loss and degradation of wetland ecosystems.
We introduce the concept of Complementary formula(COMF), which is a new and non-equivalent way for knowledge compilation(KC). Based on the Hyper extension rule(HER) which is an expansion of Extension rule(ER), we desi...
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We introduce the concept of Complementary formula(COMF), which is a new and non-equivalent way for knowledge compilation(KC). Based on the Hyper extension rule(HER) which is an expansion of Extension rule(ER), we design a compilation algorithm which can formula compile each Conjunctive normal form(CNF)formula to complementary Fully complementary connected diagram(c-FCCD), named as C2C(CNF formula to cFCCD). Theoretically, c-FCCD is a kind of complementary formulae of the input formulae and can support all queries and partial transformations in KC map. Experimentally,C2C is competitive with the EPCCL compilers KCER,C2E, UKCHER, DKCHER and IKCHER.
Directly predicting human epidermal growth factor receptor 2 (HER2) status from widely available hematoxylin and eosin (HE)-stained whole slide images (WSIs) can reduce technical costs and expedite treatment selection...
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Relation extraction is a crucial task within information extraction, and numerous models have demonstrated impressive results. However, most of the tagging-based relation triple extraction methods employ unidirectiona...
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ISBN:
(数字)9798350349184
ISBN:
(纸本)9798350349191
Relation extraction is a crucial task within information extraction, and numerous models have demonstrated impressive results. However, most of the tagging-based relation triple extraction methods employ unidirectional approaches to extract subjects, objects, and relations, which may overlook crucial information. In this paper, we introduce a novel deep matrix-based bidirectional relation extraction model. Firstly, we extract forward and backward entity pairs. During the bidirectional extraction process,there may be some redundant relationships,so we use a shared encoder to connect and enhance the extraction process. Secondly, we design a low-complexity relation extraction matrix to allocate all possible relations. We assess our model using diverse benchmark datasets, and comprehensive experiments show that our approach effectively addresses subsequent triple extraction issues stemming from entity extraction failures.
Vessel dynamics simulation is vital in studying the relationship between geometry and vascular disease progression. Reliable dynamics simulation relies on high-quality vascular meshes. Most of the existing mesh genera...
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The primary objective of medical image segmentation is the isolation of pathological tissues and distinct organs from medical images, thereby assisting in medical diagnosis. The methods employed for medical image segm...
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ISBN:
(数字)9798350349184
ISBN:
(纸本)9798350349191
The primary objective of medical image segmentation is the isolation of pathological tissues and distinct organs from medical images, thereby assisting in medical diagnosis. The methods employed for medical image segmentation encompass convolutional neural networks and transformer-based methods. Although the self-attention mechanism in Transformers improves its capability to capture long-range dependencies, it have limitations in learning local (contextual) relationships between pixels. Some previous research has tried to solve this problem by incorporating convolutional layers into the encoder or decoder of transformers, but sometimes feature inconsistencies arise. To better extract local features from images using convolutional neural networks and to fuse low-resolution and high-resolution features from higher-level and lower features, we propose the Convolutional Attention Augmentation TransUNet (CAA-TransUNet) model. In our model, firstly, we propose a convolutional attention augmentation module that enhances both local and global features by suppressing irrelevant background information. Secondly, we have integrated attention gates into the skip connections to aggregate feature information from various stages of the encoder during the upsampling process. Finally, we employ the technique of aggregating the loss of multi-stage features to expedite convergence speed and enhance performance. The experimental results on three public datasets demonstrate that our proposed model significantly outperforms the baseline methods.
The Roller-Quadrotor is a novel quadrotor that combines the maneuverability of aerial drones with the endurance of ground vehicles. This work focuses on the design, modeling, and experimental validation of the Roller-...
The Roller-Quadrotor is a novel quadrotor that combines the maneuverability of aerial drones with the endurance of ground vehicles. This work focuses on the design, modeling, and experimental validation of the Roller-Quadrotor. Flight capabilities are achieved through a quadrotor config-uration, with four thrust-providing actuators. Additionally, rolling motion is facilitated by a unicycle-driven and rotor-assisted turning structure. By utilizing terrestrial locomotion, the vehicle can overcome rolling and turning resistance, thereby conserving energy compared to its flight mode. This innovative approach not only tackles the inherent challenges of traditional rotorcraft but also enables the vehicle to roll through narrow gaps and overcome obstacles by taking advantage of its aerial mobility. We develop comprehensive models and controllers for the Roller-Quadrotor and validate their performance through experiments. The results demonstrate its seamless transition between aerial and terrestrial locomotion, as well as its ability to safely roll through gaps half the size of its diameter. Moreover, the terrestrial range of the vehicle is approximately 2.8 times greater, while the operating time is about 41.2 times longer compared to its aerial capabilities. These findings underscore the feasibility and effectiveness of the proposed structure and control mechanisms for efficient rolling through challenging terrains while conserving energy.
Background: Volumetric segmentation is crucial for medical imaging applications but faces significant challenges. Current approaches often require extensive manual annotations and scenario-specific model training, lim...
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Background: Volumetric segmentation is crucial for medical imaging applications but faces significant challenges. Current approaches often require extensive manual annotations and scenario-specific model training, limiting their transferability across different tasks or modalities. While general segmentation models offer some versatility in natural image processing, they struggle with the unique characteristics of medical images. There is an urgent need in clinical practice for a new segmentation approach that can effectively handle medical imagery features while maintaining adaptability across various three-dimensional objects and imaging modalities. Methods: We introduce PAM (Propagating Anything Model), a propagation-based segmentation approach that operates on 3D medical image volumes using a 2D prompt (bounding box or sketch mask). PAM extrapolates this initial input to generate a complete 3D segmentation by modeling inter-slice structural relationships, establishing a continuous information flow within 3D medical structures. This approach enhances segmentation effectiveness across various imaging modalities by focusing on structural and semantic continuities rather than isolating specific objects. The model combines a CNN-based UNet architecture for intra-slice information processing with a Transformer-based attention module to facilitate inter-slice propagation. This innovative framework results in a method with unique generalizability, capable of segmenting diverse 3D objects across different medical imaging modalities. Results: PAM demonstrated superior performance on 44 diverse medical datasets, notably improving the dice similarity coefficient (DSC) for hundreds of segmentation object types and various medical imaging modalities. Compared to modern models like MedSAM and SegVol, PAM achieved an average DSC improvement of over 18.1%, while maintaining stable predictions despite prompt deviation (one-way ANOVA test, P ≥ 0.5985) and varying propagation confi
Embedding-based retrieval methods construct vector indices to search for document representations that are most similar to the query representations. They are widely used in document retrieval due to low latency and d...
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