Visual order is one of the key factors influencing the aesthetic judgment of artworks.This paper reports the results of evaluating the influence of extracted features on visual order in Chinese ink paintings,using a r...Visual order is one of the key factors influencing the aesthetic judgment of artworks.This paper reports the results of evaluating the influence of extracted features on visual order in Chinese ink paintings,using a regression model.We use nine contemporary artists’paintings as examples and extract features related to the visual order of their paintings.A questionnaire survey is conducted to collect people’s rating scores on the visual order.Via regression modeling,our research analyzes the significance of each feature and validates the influences of the features on the visual order.展开更多
Using a near-infrared(NIR)light flood-illumination imager equipped with a high-speed(120 Hz)CCD camera,we demonstrated optical imaging of stimulus-evoked retinal activity in isolated,but intact,frog eye.Both fast and ...Using a near-infrared(NIR)light flood-illumination imager equipped with a high-speed(120 Hz)CCD camera,we demonstrated optical imaging of stimulus-evoked retinal activity in isolated,but intact,frog eye.Both fast and slow transient intrinsic optical signals(IOSs)were observed.Fast optical response occurred immediately after the stimulus onset,could reach peak magnitude within 100 ms,and correlated tightly with ON and OFF edges of the visible light stimulus;while slow optical response lasted a relatively long time(many seconds).High-resolution images revealed both positive(increasing)and negative(decreasing)IOSs,and dynamic optical change at individual CCD pixels could often exceed 10%of the background light intensity.Our experiment on isolated eye suggests that further development of fast,high(sub-cellular)resolution fundus imager will allow robust detection of fast IOSs in vivo,and thus allow noninvasive,three-dimensional evaluation of retinal neural function.展开更多
The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issu...The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issue is the formalisation of extracting meaning from information.Meaning emerges through a three-stage interpretative process,where the spectrum of possible interpretations is collapsed into a singular outcome by a particular context.However,this approach currently lacks practical grounding.In this research,we developed a model based on contexts,which applies interpretation principles to the visual information to address this gap.The field of computer vision and object recognition has progressed essentially with artificial neural networks,but these models struggle with geometrically transformed images,such as those that are rotated or shifted,limiting their robustness in real-world applications.Various approaches have been proposed to address this problem.Some of them(Hu moments,spatial transformers,capsule networks,attention and memory mechanisms)share a conceptual connection with the contextual model(CM)discussed in this study.This paper investigates whether CM principles are applicable for interpreting rotated images from the MNIST and Fashion MNIST datasets.The model was implemented in the Rust programming language.It consists of a contextual module and a convolutional neural network(CNN).The CMwas trained on the rotated Mono Icons dataset,which is significantly different from the testing datasets.The CNN module was trained on the original MNIST and Fashion MNIST datasets for interpretation recognition.As a result,the CM was able to recognise the original datasets but encountered rotated images only during testing.The findings show that the model effectively interpreted transformed images by considering them in all available contexts and restoring their original form.This provides a practical foundation for further development of the contextual hypothesis and its relation to theAGI domain.展开更多
文摘Visual order is one of the key factors influencing the aesthetic judgment of artworks.This paper reports the results of evaluating the influence of extracted features on visual order in Chinese ink paintings,using a regression model.We use nine contemporary artists’paintings as examples and extract features related to the visual order of their paintings.A questionnaire survey is conducted to collect people’s rating scores on the visual order.Via regression modeling,our research analyzes the significance of each feature and validates the influences of the features on the visual order.
文摘Using a near-infrared(NIR)light flood-illumination imager equipped with a high-speed(120 Hz)CCD camera,we demonstrated optical imaging of stimulus-evoked retinal activity in isolated,but intact,frog eye.Both fast and slow transient intrinsic optical signals(IOSs)were observed.Fast optical response occurred immediately after the stimulus onset,could reach peak magnitude within 100 ms,and correlated tightly with ON and OFF edges of the visible light stimulus;while slow optical response lasted a relatively long time(many seconds).High-resolution images revealed both positive(increasing)and negative(decreasing)IOSs,and dynamic optical change at individual CCD pixels could often exceed 10%of the background light intensity.Our experiment on isolated eye suggests that further development of fast,high(sub-cellular)resolution fundus imager will allow robust detection of fast IOSs in vivo,and thus allow noninvasive,three-dimensional evaluation of retinal neural function.
文摘The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issue is the formalisation of extracting meaning from information.Meaning emerges through a three-stage interpretative process,where the spectrum of possible interpretations is collapsed into a singular outcome by a particular context.However,this approach currently lacks practical grounding.In this research,we developed a model based on contexts,which applies interpretation principles to the visual information to address this gap.The field of computer vision and object recognition has progressed essentially with artificial neural networks,but these models struggle with geometrically transformed images,such as those that are rotated or shifted,limiting their robustness in real-world applications.Various approaches have been proposed to address this problem.Some of them(Hu moments,spatial transformers,capsule networks,attention and memory mechanisms)share a conceptual connection with the contextual model(CM)discussed in this study.This paper investigates whether CM principles are applicable for interpreting rotated images from the MNIST and Fashion MNIST datasets.The model was implemented in the Rust programming language.It consists of a contextual module and a convolutional neural network(CNN).The CMwas trained on the rotated Mono Icons dataset,which is significantly different from the testing datasets.The CNN module was trained on the original MNIST and Fashion MNIST datasets for interpretation recognition.As a result,the CM was able to recognise the original datasets but encountered rotated images only during testing.The findings show that the model effectively interpreted transformed images by considering them in all available contexts and restoring their original form.This provides a practical foundation for further development of the contextual hypothesis and its relation to theAGI domain.
