Recent days,Image retrieval has become a tedious process as the image database has grown very larger.The introduction of Machine Learning(ML)and Deep Learning(DL)made this process more comfortable.In these,the pair-wi...Recent days,Image retrieval has become a tedious process as the image database has grown very larger.The introduction of Machine Learning(ML)and Deep Learning(DL)made this process more comfortable.In these,the pair-wise label similarity is used tofind the matching images from the database.But this method lacks of limited propose code and weak execution of misclassified images.In order to get-rid of the above problem,a novel triplet based label that incorporates context-spatial similarity measure is proposed.A Point Attention Based Triplet Network(PABTN)is introduced to study propose code that gives maximum discriminative ability.To improve the performance of ranking,a corre-lating resolutions for the classification,triplet labels based onfindings,a spatial-attention mechanism and Region Of Interest(ROI)and small trial information loss containing a new triplet cross-entropy loss are used.From the experimental results,it is shown that the proposed technique exhibits better results in terms of mean Reciprocal Rank(mRR)and mean Average Precision(mAP)in the CIFAR-10 and NUS-WIPE datasets.展开更多
Visual question answering(VQA)requires a deep understanding of images and their corresponding textual questions to answer questions about images more accurately.However,existing models tend to ignore the implicit know...Visual question answering(VQA)requires a deep understanding of images and their corresponding textual questions to answer questions about images more accurately.However,existing models tend to ignore the implicit knowledge in the images and focus only on the visual information in the images,which limits the understanding depth of the image content.The images contain more than just visual objects,some images contain textual information about the scene,and slightly more complex images contain relationships between individual visual objects.Firstly,this paper proposes a model using image description for feature enhancement.This model encodes images and their descriptions separately based on the question-guided coattention mechanism.This mechanism increases the feature representation of the model,enhancing the model’s ability for reasoning.In addition,this paper improves the bottom-up attention model by obtaining two image region features.After obtaining the two visual features and the spatial position information corresponding to each feature,concatenating the two features as the final image feature can better represent an image.Finally,the obtained spatial position information is processed to enable the model to perceive the size and relative position of each object in the image.Our best single model delivers a 74.16%overall accuracy on the VQA 2.0 dataset,our model even outperforms some multi-modal pre-training models with fewer images and a shorter time.展开更多
Background The serotonin transporter(SERT),encoded by the solute carrier family 6 number 4(SLC6A4)gene,controls serotonin(5-HT)availability and is essential for the regulation of behavioral traits.Two SLC6A4 genetic v...Background The serotonin transporter(SERT),encoded by the solute carrier family 6 number 4(SLC6A4)gene,controls serotonin(5-HT)availability and is essential for the regulation of behavioral traits.Two SLC6A4 genetic variants,5-HTTLPR and STin2,were widely investigated in patients with various neurobehavioral disorders,including attention deficit hyperactivity disorder(ADHD).Methods We analyzed the association of the 5-HTTLPR(L/S)and STin2(10/12)variants,plasma 5-HT,and 5-hydroxyindole acetic acid(5-HIAA),as well as SERT messenger RNA(mRNA)with ADHD in the eastern Indian subjects.Nuclear families with ADHD probands(n=274)and ethnically matched controls(n=367)were recruited following the Diagnostic and Statistical Manual of Mental Disorders.Behavioral traits,executive function,and intelligence quotient(IQ)of the probands were assessed using the Conner's Parent Rating Scale–Revised,Parental Account of Children’s Symptoms(PACS),Barkley Deficit in Executive Functioning—Child and Adolescent Scale,and Wechsler Intelligence Scale for Children-III,respectively.After obtaining informed written consent,peripheral blood was collected to analyze genetic variants,plasma 5-HT,5-HIAA,and SERT mRNA expression.Results ADHD probands showed a higher frequency of the 5-HTTLPR“L”allele and“L/L”genotype(P<0.05),lower 5-HIAA level,and higher SERT mRNA expression.Scores for behavioral problems and hyperactivity were higher in the presence of the“S”allele and“S/S”genotype,while executive deficit was higher in the presence of the“L”allele.IQ score was lower in the presence of the STin2“12”allele and L-12 haplotype.Conclusion Data obtained indicate a significant association of the serotoninergic system with ADHD,warranting further in-depth investigation.展开更多
文摘Recent days,Image retrieval has become a tedious process as the image database has grown very larger.The introduction of Machine Learning(ML)and Deep Learning(DL)made this process more comfortable.In these,the pair-wise label similarity is used tofind the matching images from the database.But this method lacks of limited propose code and weak execution of misclassified images.In order to get-rid of the above problem,a novel triplet based label that incorporates context-spatial similarity measure is proposed.A Point Attention Based Triplet Network(PABTN)is introduced to study propose code that gives maximum discriminative ability.To improve the performance of ranking,a corre-lating resolutions for the classification,triplet labels based onfindings,a spatial-attention mechanism and Region Of Interest(ROI)and small trial information loss containing a new triplet cross-entropy loss are used.From the experimental results,it is shown that the proposed technique exhibits better results in terms of mean Reciprocal Rank(mRR)and mean Average Precision(mAP)in the CIFAR-10 and NUS-WIPE datasets.
基金supported in part by the National Natural Science Foundation of China under Grant U1911401.
文摘Visual question answering(VQA)requires a deep understanding of images and their corresponding textual questions to answer questions about images more accurately.However,existing models tend to ignore the implicit knowledge in the images and focus only on the visual information in the images,which limits the understanding depth of the image content.The images contain more than just visual objects,some images contain textual information about the scene,and slightly more complex images contain relationships between individual visual objects.Firstly,this paper proposes a model using image description for feature enhancement.This model encodes images and their descriptions separately based on the question-guided coattention mechanism.This mechanism increases the feature representation of the model,enhancing the model’s ability for reasoning.In addition,this paper improves the bottom-up attention model by obtaining two image region features.After obtaining the two visual features and the spatial position information corresponding to each feature,concatenating the two features as the final image feature can better represent an image.Finally,the obtained spatial position information is processed to enable the model to perceive the size and relative position of each object in the image.Our best single model delivers a 74.16%overall accuracy on the VQA 2.0 dataset,our model even outperforms some multi-modal pre-training models with fewer images and a shorter time.
文摘Background The serotonin transporter(SERT),encoded by the solute carrier family 6 number 4(SLC6A4)gene,controls serotonin(5-HT)availability and is essential for the regulation of behavioral traits.Two SLC6A4 genetic variants,5-HTTLPR and STin2,were widely investigated in patients with various neurobehavioral disorders,including attention deficit hyperactivity disorder(ADHD).Methods We analyzed the association of the 5-HTTLPR(L/S)and STin2(10/12)variants,plasma 5-HT,and 5-hydroxyindole acetic acid(5-HIAA),as well as SERT messenger RNA(mRNA)with ADHD in the eastern Indian subjects.Nuclear families with ADHD probands(n=274)and ethnically matched controls(n=367)were recruited following the Diagnostic and Statistical Manual of Mental Disorders.Behavioral traits,executive function,and intelligence quotient(IQ)of the probands were assessed using the Conner's Parent Rating Scale–Revised,Parental Account of Children’s Symptoms(PACS),Barkley Deficit in Executive Functioning—Child and Adolescent Scale,and Wechsler Intelligence Scale for Children-III,respectively.After obtaining informed written consent,peripheral blood was collected to analyze genetic variants,plasma 5-HT,5-HIAA,and SERT mRNA expression.Results ADHD probands showed a higher frequency of the 5-HTTLPR“L”allele and“L/L”genotype(P<0.05),lower 5-HIAA level,and higher SERT mRNA expression.Scores for behavioral problems and hyperactivity were higher in the presence of the“S”allele and“S/S”genotype,while executive deficit was higher in the presence of the“L”allele.IQ score was lower in the presence of the STin2“12”allele and L-12 haplotype.Conclusion Data obtained indicate a significant association of the serotoninergic system with ADHD,warranting further in-depth investigation.
