Computational optics introduces computation into optics and consequently helps overcome traditional optical limitations such as low sensing dimension,low light throughput,low resolution,and so on.The combination of op...Computational optics introduces computation into optics and consequently helps overcome traditional optical limitations such as low sensing dimension,low light throughput,low resolution,and so on.The combination of optical encoding and computational decoding offers enhanced imaging and sensing capabilities with diverse applications in biomedicine,astronomy,agriculture,etc.With the great advance of artificial intelligence in the last decade,deep learning has further boosted computational optics with higher precision and efficiency.Recently,there developed an end-to-end joint optimization technique that digitally twins optical encoding to neural network layers,and then facilitates simultaneous optimization with the decoding process.This framework offers effective performance enhancement over conventional techniques.However,the reverse physical twinning from optimized encoding parameters to practical modulation elements faces a serious challenge,due to the discrepant gap in such as bit depth,numerical range,and stability.In this regard,this review explores various optical modulation elements across spatial,phase,and spectral dimensions in the digital twin model for joint encoding-decoding optimization.Our analysis offers constructive guidance for finding the most appropriate modulation element in diverse imaging and sensing tasks concerning various requirements of precision,speed,and robustness.The review may help tackle the above twinning challenge and pave the way for next-generation computational optics.展开更多
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62131003,62322502,62088101)the Guangdong Province Key Laboratory of Intelligent Detection in Complex Environment of Aerospace,Land and Sea(No.2022KSYS016).
文摘Computational optics introduces computation into optics and consequently helps overcome traditional optical limitations such as low sensing dimension,low light throughput,low resolution,and so on.The combination of optical encoding and computational decoding offers enhanced imaging and sensing capabilities with diverse applications in biomedicine,astronomy,agriculture,etc.With the great advance of artificial intelligence in the last decade,deep learning has further boosted computational optics with higher precision and efficiency.Recently,there developed an end-to-end joint optimization technique that digitally twins optical encoding to neural network layers,and then facilitates simultaneous optimization with the decoding process.This framework offers effective performance enhancement over conventional techniques.However,the reverse physical twinning from optimized encoding parameters to practical modulation elements faces a serious challenge,due to the discrepant gap in such as bit depth,numerical range,and stability.In this regard,this review explores various optical modulation elements across spatial,phase,and spectral dimensions in the digital twin model for joint encoding-decoding optimization.Our analysis offers constructive guidance for finding the most appropriate modulation element in diverse imaging and sensing tasks concerning various requirements of precision,speed,and robustness.The review may help tackle the above twinning challenge and pave the way for next-generation computational optics.
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
