ASO Visual Abstract: Primary Breast Neuroendocrine Tumors—An Analysis of the National Cancer Database

Martínez, Enrique Ortega; Jorns, Julie M.; Kong, Amanda L.; Kijak, Julie A.; Lee, Wen-Yao; Huang, Chiang-Ching; Cortina, Chandler S.

doi:10.1245/s10434-022-12409-z

Cited by 1 publication

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“…The reverse sentiment reward is set as in equation (10), and the Q value is updated as in equation (11), with the reverse direction at the time of entering this node state.…”

Section: Incentive Mechanismmentioning

confidence: 99%

“…For nearly two decades, AI researchers have been trying to empower machines with cognitive abilities to recognize, interpret, and express emotions [9]. Artificial intelligence techniques simulate human emotional cognition and decisionmaking processes by correlating, analyzing and reconstructing data containing emotional information in different scenarios with each other, and eventually transforming the data into abstracted thoughts that computers can understand [10][11][12]. The affective interaction process uses the user's modal data to achieve recognition of the user's affective state and uses the feedback information from the affect recognition to perform affective modeling based on cognitive analysis and to guide the interaction behavior [13][14].…”

Section: Introductionmentioning

confidence: 99%

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Exploring the direction of the English translation of environmental protection articles based on the robot cognitive- emotional interaction model

Shi¹

2023

3C TIC

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To broaden the application area of the cognitive-emotional interaction model for robots. In this paper, an algorithmic model for the English translation of environmental articles based on a cognitive-emotional interaction model for robots is used to model the process of emotion generation using reinforcement learning. Similarly, positivity and empathy are used to quantify the reward function for emotional state assessment, and the optimal emotional strategy selection is derived based on the utility function. In the process of article translation by the robot, Lagrangian factors are introduced to make the translation probability maximum process transformed into the process of obtaining the highest value of the auxiliary function at a random state. Finally, the effectiveness of the robot's cognitive-emotional interaction model in the English translation of environmental protection articles is verified by the Chinese-English parallel question-and-answer dataset. The experimental results demonstrate that this model can not only be used for the English translation of environmental protection articles but also can give the corresponding English translation work similar to human emotions, which can better help people understand the meaning of English. It also provides a basis and direction for the subsequent in-depth application of the robot cognitive-emotional interaction model in various fields.

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“…The reverse sentiment reward is set as in equation (10), and the Q value is updated as in equation (11), with the reverse direction at the time of entering this node state.…”

Section: Incentive Mechanismmentioning

confidence: 99%