The JPEG2000 image compression standard is the powerful encoder which can provide phenomenal rate-control performance.The post-compression rate-distortion(PCRD)algorithm in JPEG2000 is not efficient.It requires encodi...The JPEG2000 image compression standard is the powerful encoder which can provide phenomenal rate-control performance.The post-compression rate-distortion(PCRD)algorithm in JPEG2000 is not efficient.It requires encoding all coding passes even though a large contribution of them will not be contained in the final code-stream.Tier-1 encoding in the JPEG2000 standard takes a significant amount of memory and coding time.In this work,a low-complexity rate distortion method for JPEG2000 is proposed.It is relied on a reverse order for the resolution levels and the coding passes.The proposed algorithm encodes only the coding passes contained in the final code-stream and it does not need any post compression rate control part.The computational complexity of proposed algorithm is negligible,making it suitable to compression and attaining a significant performance.Simulations results show that the proposed algorithm obtained the PSNR values are comparable with the optimal PCRD.展开更多
采用两步 PCR法成功地克隆了一个全长的 c DNA.首先 ,用差式分析法克隆得到差别表达的 c DNA片段 ,再分别用这些片段内部的特异序列及 c DNA两端不同接头的序列为引物进行第一步 PCR扩增 ,得到差别 c DNA片段的上游和下游序列 .然后 ,...采用两步 PCR法成功地克隆了一个全长的 c DNA.首先 ,用差式分析法克隆得到差别表达的 c DNA片段 ,再分别用这些片段内部的特异序列及 c DNA两端不同接头的序列为引物进行第一步 PCR扩增 ,得到差别 c DNA片段的上游和下游序列 .然后 ,根据第一步 PCR扩增得到的上游和下游序列设计基因特异的引物进行第二步 PCR,从而得到全长的 c DNA.展开更多
Knowledge distillation has demonstrated considerable success in scenarios involving multi-class single-label learning.However,its direct application to multi-label learning proves challenging due to complex correlatio...Knowledge distillation has demonstrated considerable success in scenarios involving multi-class single-label learning.However,its direct application to multi-label learning proves challenging due to complex correlations in multi-label structures,causing student models to overlook more finely structured semantic relations present in the teacher model.In this paper,we present a solution called multi-label prototype-aware structured contrastive distillation,comprising two modules:Prototype-aware Contrastive Representation Distillation(PCRD)and prototype-aware cross-image structure distillation.The PCRD module maximizes the mutual information of prototype-aware representation between the student and teacher,ensuring semantic representation structure consistency to improve the compactness of intra-class and dispersion of inter-class representations.In the PCSD module,we introduce sample-to-sample and sample-to-prototype structured contrastive distillation to model prototype-aware cross-image structure consistency,guiding the student model to maintain a coherent label semantic structure with the teacher across multiple instances.To enhance prototype guidance stability,we introduce batch-wise dynamic prototype correction for updating class prototypes.Experimental results on three public benchmark datasets validate the effectiveness of our proposed method,demonstrating its superiority over state-of-the-art methods.展开更多
文摘The JPEG2000 image compression standard is the powerful encoder which can provide phenomenal rate-control performance.The post-compression rate-distortion(PCRD)algorithm in JPEG2000 is not efficient.It requires encoding all coding passes even though a large contribution of them will not be contained in the final code-stream.Tier-1 encoding in the JPEG2000 standard takes a significant amount of memory and coding time.In this work,a low-complexity rate distortion method for JPEG2000 is proposed.It is relied on a reverse order for the resolution levels and the coding passes.The proposed algorithm encodes only the coding passes contained in the final code-stream and it does not need any post compression rate control part.The computational complexity of proposed algorithm is negligible,making it suitable to compression and attaining a significant performance.Simulations results show that the proposed algorithm obtained the PSNR values are comparable with the optimal PCRD.
文摘采用两步 PCR法成功地克隆了一个全长的 c DNA.首先 ,用差式分析法克隆得到差别表达的 c DNA片段 ,再分别用这些片段内部的特异序列及 c DNA两端不同接头的序列为引物进行第一步 PCR扩增 ,得到差别 c DNA片段的上游和下游序列 .然后 ,根据第一步 PCR扩增得到的上游和下游序列设计基因特异的引物进行第二步 PCR,从而得到全长的 c DNA.
基金supported by the National Natural Science Foundation of China(No.62466061)the Yunnan Fundamental Research Projects(No.202401AU070052)+1 种基金the Yunnan Provincial Department of Education Science Research Fund,China(Nos.2023J0209 and 2024Y161)the Natural Science Doctoral Research Start-Up Fund of Yunnan Normal University(No.2022ZB015).
文摘Knowledge distillation has demonstrated considerable success in scenarios involving multi-class single-label learning.However,its direct application to multi-label learning proves challenging due to complex correlations in multi-label structures,causing student models to overlook more finely structured semantic relations present in the teacher model.In this paper,we present a solution called multi-label prototype-aware structured contrastive distillation,comprising two modules:Prototype-aware Contrastive Representation Distillation(PCRD)and prototype-aware cross-image structure distillation.The PCRD module maximizes the mutual information of prototype-aware representation between the student and teacher,ensuring semantic representation structure consistency to improve the compactness of intra-class and dispersion of inter-class representations.In the PCSD module,we introduce sample-to-sample and sample-to-prototype structured contrastive distillation to model prototype-aware cross-image structure consistency,guiding the student model to maintain a coherent label semantic structure with the teacher across multiple instances.To enhance prototype guidance stability,we introduce batch-wise dynamic prototype correction for updating class prototypes.Experimental results on three public benchmark datasets validate the effectiveness of our proposed method,demonstrating its superiority over state-of-the-art methods.
