As ocular computer-aided diagnostic(CAD)tools become more widely accessible,many researchers are developing deep learning(DL)methods to aid in ocular disease(OHD)*** eye diseases like cataracts(CATR),glaucoma(GLU),and...
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As ocular computer-aided diagnostic(CAD)tools become more widely accessible,many researchers are developing deep learning(DL)methods to aid in ocular disease(OHD)*** eye diseases like cataracts(CATR),glaucoma(GLU),and age-related macular degeneration(AMD)are the focus of this study,which uses DL to examine their *** imbalance and outliers are widespread in fundus images,which can make it difficult to apply manyDL algorithms to accomplish this analytical *** creation of efficient and reliable DL algorithms is seen to be the key to further enhancing detection *** the analysis of images of the color of the retinal fundus,this study offers a DL model that is combined with a one-of-a-kind concoction loss function(CLF)for the automated identification of *** study presents a combination of focal loss(FL)and correntropy-induced loss functions(CILF)in the proposed DL model to improve the recognition performance of classifiers for biomedical *** is done because of the good generalization and robustness of these two types of losses in addressing complex datasets with class imbalance and *** classification performance of the DL model with our proposed loss function is compared to that of the baseline models using accuracy(ACU),recall(REC),specificity(SPF),Kappa,and area under the receiver operating characteristic curve(AUC)as the evaluation *** testing shows that the method is reliable and efficient.
Due to the insufficient semantic information supervision in existing works for dynamic facial expression recognition (DFER), videos with similar facial changes but different expressions may be easily confused. Thanks ...
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Nowadays, several plagiarism detection tools based on static code features are available for code similarity detection. The application of deep learning in this domain represents an emerging area of research. This res...
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Representation learning on spatial networks is emerging as a distinct area in machine learning and is attracting much attention in diverse domains. Some applications include molecular graphs for drug discovery and scr...
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Deep neural networks (DNNs) have recently achieved remarkable advancements in time series forecasting (TSF) due to their powerful ability of sequence dependence modeling. To date, existing DNN-based TSF methods still ...
CNNs have shown remarkable performance on a variety of computer vision problems. However, CNN-based models require a lot of computational resources, which have limitations of resource-constrained environments. To addr...
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Inheritance, a fundamental aspect of object-oriented design, has been leveraged to enhance code reuse and facilitate efficient software development. However, alongside its benefits, inheritance can introduce tight cou...
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Three-dimensional (3D) graph representations have gained significant importance in numerous scientific domains, such as molecular dynamics and astrophysics. In these applications, a precise and effective representatio...
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Weakly-supervised Temporal Action Localization (WTAL) following a localization-by-classification paradigm has achieved significant results, yet still grapples with confounding arising from ambiguous snippets. Previous...
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High-performance computing and deep learning domains have been motivating the design of domain-specific *** these processors can provide promising computation capability,they are notorious for exotic programming *** i...
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High-performance computing and deep learning domains have been motivating the design of domain-specific *** these processors can provide promising computation capability,they are notorious for exotic programming *** improve programming productivity and fully exploit the performance potential of these processors,domain-specific compilers(DSCs) have been ***,building DSCs for emerging processors requires tremendous engineering efforts because the commonly used compilation stack is difficult to be *** to the advent of multilevel intermediate representation(MLIR),DSC developers can leverage reusable infrastructure to extend their customized functionalities without rebuilding the entire compilation *** this paper,we further demonstrate the effectiveness of MLIR by extending its reusable infrastructure to embrace a heterogeneous many-core processor(Sunway processor).In particular,we design a new Sunway dialect and corresponding backend for the Sunway processor,fully exploiting its architectural advantage and hiding its programming *** show the ease of building a DSC,we leverage the Sunway dialect and existing MLIR dialects to build a stencil compiler for the Sunway *** experimental results show that our stencil compiler,built with a reusable approach,can even perform better than state-of-the-art stencil compilers.
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