The application of omics technologies,including genomics,transcriptomics,proteomics and metabolomics,has the potential to revolutionize toxicology by providing a more comprehensive understanding of the molecular mecha...The application of omics technologies,including genomics,transcriptomics,proteomics and metabolomics,has the potential to revolutionize toxicology by providing a more comprehensive understanding of the molecular mechanisms of toxicity,identifying potential biomarkers of exposure or effect,and enabling personalized risk assessments for individuals.Each omics approach has its own challenges,including data analysis and interpretation,but the integration of multiple omics approaches can provide a more comprehensive understanding of toxicity.The use of omics technologies for personalized risk assessments can inform targeted interventions and improve public health outcomes.While challenges remain,the potential benefits of omics technologies in toxicology make it an exciting area of research for the future.展开更多
Recent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks(CNNs).However,these methods focus primarily on predicting generally perceived pref...Recent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks(CNNs).However,these methods focus primarily on predicting generally perceived preference of an image,making them usually have limited practicability,since each user may have completely different preferences for the same image.To address this problem,this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste.We achieve this in a coarse to fine manner,by joint regression and learning from pairwise rankings.Specifically,we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs.We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores,and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression.Next,we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss.Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences,clearly outperforming state-of-the-art methods.Moreover,we show that the learned personalized image aesthetic benefits a wide variety of applications.展开更多
文摘The application of omics technologies,including genomics,transcriptomics,proteomics and metabolomics,has the potential to revolutionize toxicology by providing a more comprehensive understanding of the molecular mechanisms of toxicity,identifying potential biomarkers of exposure or effect,and enabling personalized risk assessments for individuals.Each omics approach has its own challenges,including data analysis and interpretation,but the integration of multiple omics approaches can provide a more comprehensive understanding of toxicity.The use of omics technologies for personalized risk assessments can inform targeted interventions and improve public health outcomes.While challenges remain,the potential benefits of omics technologies in toxicology make it an exciting area of research for the future.
基金supported partially by the National Key Research and Development Program of China(2018YFB1004903)National Natural Science Foundation of China(61802453,U1911401,U1811461)+1 种基金Fundamental Research Funds for the Central Universities(19lgpy216)Research Projects of Zhejiang Lab(2019KD0AB03).
文摘Recent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks(CNNs).However,these methods focus primarily on predicting generally perceived preference of an image,making them usually have limited practicability,since each user may have completely different preferences for the same image.To address this problem,this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste.We achieve this in a coarse to fine manner,by joint regression and learning from pairwise rankings.Specifically,we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs.We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores,and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression.Next,we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss.Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences,clearly outperforming state-of-the-art methods.Moreover,we show that the learned personalized image aesthetic benefits a wide variety of applications.
