Object tracking is widely used in daily life. The existing visible camera-based tracking methods are difficult to track objects accurately in the face of degraded scenes such as fast movement, high contrast, and low i...
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Recent advances in spatially resolved transcriptomic technologies have enabled unprecedented opportunities to elucidate tissue architecture and function in *** transcriptomics can provide multimodal and complementary ...
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Recent advances in spatially resolved transcriptomic technologies have enabled unprecedented opportunities to elucidate tissue architecture and function in *** transcriptomics can provide multimodal and complementary information simultaneously,including gene expression profiles,spatial locations,and histology ***,most existing methods have limitations in efficiently utilizing spatial information and matched high-resolution histology *** fully leverage the multi-modal information,we propose a SPAtially embedded Deep Attentional graph Clustering(SpaDAC)method to identify spatial domains while reconstructing denoised gene expression *** method can efficiently learn the low-dimensional embeddings for spatial transcriptomics data by constructing multi-view graph modules to capture both spatial location connectives and morphological *** results demonstrate that SpaDAC outperforms other algorithms on several recent spatial transcriptomics *** is a valuable tool for spatial domain detection,facilitating the comprehension of tissue architecture and cellular *** source code of SpaDAC is freely available at Github(https://***/huoyuying/***).
Strong physical unclonable function (PUF) is a low-cost hardware security primitive to protect Internet-of-Things (IoT) devices. However, it may be attacked by machine learning (ML). Various PUF models designed in com...
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Various temporal denoising methods have been proposed to clean up the noise for real-time ray tracing (RTRT). These methods rely on the temporal correspondences of pixels between the current and previous frames, i.e. ...
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Various temporal denoising methods have been proposed to clean up the noise for real-time ray tracing (RTRT). These methods rely on the temporal correspondences of pixels between the current and previous frames, i.e. per-pixel screen-space motion vectors. However, the state-of-the-art temporal reuse methods with traditional motion vectors cause artifacts in motion occlusions. We accordingly propose a novel neural temporal denoising method for indirect illumination of Monte Carlo (MC) ray tracing at 1 sample per pixel. Based on end-to-end multi-scale kernel-based reconstruction, we apply temporally reliable dual motion vectors to facilitate better reconstruction of the occlusions, and also introduce additional motion occlusion loss to reduce ghosting artifacts. Experiments show that our method significantly reduces the over-blurring and ghosting artifacts while generating high-quality images at real-time rates.
Mashup developers often need to find open application programming interfaces(APIs) for their composition application development. Although most enterprises and service organizations have encapsulated their businesses ...
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Mashup developers often need to find open application programming interfaces(APIs) for their composition application development. Although most enterprises and service organizations have encapsulated their businesses or resources online as open APIs, finding the right high-quality open APIs is not an easy task from a library with several open APIs. To solve this problem, this paper proposes a deep learning-based open API recommendation(DLOAR) approach. First, the hierarchical density-based spatial clustering of applications with a noise topic model is constructed to build topic models for Mashup clusters. Second,developers' requirement keywords are extracted by the Text Rank algorithm, and the language model is built. Third, a neural network-based three-level similarity calculation is performed to find the most relevant open APIs. Finally, we complement the relevant information of open APIs in the recommended list to help developers make better choices. We evaluate the DLOAR approach on a real dataset and compare it with commonly used open API recommendation approaches: term frequency-inverse document frequency, latent dirichlet allocation, Word2Vec, and Sentence-BERT. The results show that the DLOAR approach has better performance than the other approaches in terms of precision, recall, F1-measure, mean average precision,and mean reciprocal rank.
This paper addresses the limitations of the Contrastive Language-Image Pre-training (CLIP) model’s image encoder and proposes a segmentation model WSSS-ECFE with enhanced CLIP feature extraction, aiming to improve th...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
This paper addresses the limitations of the Contrastive Language-Image Pre-training (CLIP) model’s image encoder and proposes a segmentation model WSSS-ECFE with enhanced CLIP feature extraction, aiming to improve the performance of the Weakly Supervised Semantic Segmentation (WSSS) task. WSSS-ECFE employs the Enhanced Bottleneck module proposed in this paper and adds dynamic residual connection to improve the model’s processing effect on complex scenes. In terms of implementation, the Enhanced Bottleneck module employs the Swish activation function and the Depthwise Separable Convolution to enhance the feature extraction and segmentation capability of the model, and uses multiple attention mechanisms to further optimize the feature representation and segmentation accuracy. The WSSS task on the public datasets PASCAL VOC 2012 and MS COCO 2014 achieves 82.6% and 56.3% mean intersection over union (mIoU), achieving state-of-the-art performance in models with low resource requirements.
Aiming at the control planning of inverted pendulum task, the planning strategy based on Soft Actor-Critic (SAC) algorithm was studied. An agent based on Actor-Critic framework is designed, which takes inverted pendul...
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Programmers frequently consult API documentation to learn how to use libraries, both those included with a programming language and those offered by third parties. Beginner programmers also have this need but struggle...
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ISBN:
(纸本)9798400712166
Programmers frequently consult API documentation to learn how to use libraries, both those included with a programming language and those offered by third parties. Beginner programmers also have this need but struggle to browse professional documentation systems, which are aimed at experienced programmers. Educators sometimes try to patch this problem by writing simplified, ad hoc educational documents as a surrogate for a documentation system. This paper presents Judicious, an API documentation system explicitly designed for novice programmers. It allows retrieving the documentation for one name at a time;offers a clear and distinctive visual representation of functions and constants;gradually presents more information such as types, optional and variable-length parameters for functions;highlights functions with side effects;and instantaneously generates documentation also for functions defined in student code. Judicious's design builds on prior research in the learning sciences and programming languages. The gradual disclosing of information matches the progression of increasingly larger subsets of programming languages. The diagrammatic representation, the clear distinction between functions and constants, and the pinpointing of side effects aim to address known novice misconceptions. The system is integrated into a code editor and is publicly available as a web platform.
Structures featuring flexoelectric and flexomagnetic effects have great potential for the manufacture of innovative devices, such as data storage, actuators, sensors and energy harvesters. To evaluate the stability of...
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The rapid expansion of multimedia data has made multimedia data mining a crucial area of research in the field of crime. In the realm of crime, the significance of multimedia data mining lies in its ability to reveal ...
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