Context: The advent of Artificial Intelligence (AI) requires modeling prior to its implementation in algorithms for most human skills. This observation requires us to have a detailed and precise understanding of the i...Context: The advent of Artificial Intelligence (AI) requires modeling prior to its implementation in algorithms for most human skills. This observation requires us to have a detailed and precise understanding of the interfaces of verbal and emotional communications. The progress of AI is significant on the verbal level but modest in terms of the recognition of facial emotions even if this functionality is one of the oldest in humans and is omnipresent in our daily lives. Dysfunction in the ability for facial emotional expressions is present in many brain pathologies encountered by psychiatrists, neurologists, psychotherapists, mental health professionals including social workers. It cannot be objectively verified and measured due to a lack of reliable tools that are valid and consistently sensitive. Indeed, the articles in the scientific literature dealing with Visual-Facial-Emotions-Recognition (ViFaEmRe), suffer from the absence of 1) consensual and rational tools for continuous quantified measurement, 2) operational concepts. We have invented a software that can use computer-morphing attempting to respond to these two obstacles. It is identified as the Method of Analysis and Research of the Integration of Emotions (M.A.R.I.E.). Our primary goal is to use M.A.R.I.E. to understand the physiology of ViFaEmRe in normal healthy subjects by standardizing the measurements. Then, it will allow us to focus on subjects manifesting abnormalities in this ability. Our second goal is to make our contribution to the progress of AI hoping to add the dimension of recognition of facial emotional expressions. Objective: To study: 1) categorical vs dimensional aspects of recognition of ViFaEmRe, 2) universality vs idiosyncrasy, 3) immediate vs ambivalent Emotional-Decision-Making, 4) the Emotional-Fingerprint of a face and 5) creation of population references data. Methods: With M.A.R.I.E. enable a rational quantified measurement of Emotional-Visual-Acuity (EVA) of 1) a) an individual observer, b) in a population aged 20 to 70 years old, 2) measure the range and intensity of expressed emotions by 3 Face-Tests, 3) quantify the performance of a sample of 204 observers with hyper normal measures of cognition, “thymia,” (ibid. defined elsewhere) and low levels of anxiety 4) analysis of the 6 primary emotions. Results: We have individualized the following continuous parameters: 1) “Emotional-Visual-Acuity”, 2) “Visual-Emotional-Feeling”, 3) “Emotional-Quotient”, 4) “Emotional-Deci-sion-Making”, 5) “Emotional-Decision-Making Graph” or “Individual-Gun-Trigger”6) “Emotional-Fingerprint” or “Key-graph”, 7) “Emotional-Finger-print-Graph”, 8) detecting “misunderstanding” and 9) detecting “error”. This allowed us a taxonomy with coding of the face-emotion pair. Each face has specific measurements and graphics. The EVA improves from ages of 20 to 55 years, then decreases. It does not depend on the sex of the observer, nor the face studied. In addition, 1% of people endowed with normal intelligence do not recognize emotions. The categorical dimension is a variable for everyone. The range and intensity of ViFaEmRe is idiosyncratic and not universally uniform. The recognition of emotions is purely categorical for a single individual. It is dimensional for a population sample. Conclusions: Firstly, M.A.R.I.E. has made possible to bring out new concepts and new continuous measurements variables. The comparison between healthy and abnormal individuals makes it possible to take into consideration the significance of this line of study. From now on, these new functional parameters will allow us to identify and name “emotional” disorders or illnesses which can give additional dimension to behavioral disorders in all pathologies that affect the brain. Secondly, the ViFaEmRe is idiosyncratic, categorical, and a function of the identity of the observer and of the observed face. These findings stack up against Artificial Intelligence, which cannot have a globalist or regionalist algorithm that can be programmed into a robot, nor can AI compete with human abilities and judgment in this domain. *Here “Emotional disorders” refers to disorders of emotional expressions and recognition.展开更多
Context: The advent of Artificial Intelligence (AI) requires modeling prior to its implementation in algorithms for most human skills. This observation requires us to have a detailed and precise understanding of the i...Context: The advent of Artificial Intelligence (AI) requires modeling prior to its implementation in algorithms for most human skills. This observation requires us to have a detailed and precise understanding of the interfaces of verbal and emotional communications. The progress of AI is significant on the verbal level but modest in terms of the recognition of facial emotions even if this functionality is one of the oldest in humans and is omnipresent in our daily lives. Dysfunction in the ability for facial emotional expressions is present in many brain pathologies encountered by psychiatrists, neurologists, psychotherapists, mental health professionals including social workers. It cannot be objectively verified and measured due to a lack of reliable tools that are valid and consistently sensitive. Indeed, the articles in the scientific literature dealing with Visual-Facial-Emotions-Recognition (ViFaEmRe), suffer from the absence of 1) consensual and rational tools for continuous quantified measurement, 2) operational concepts. We have invented a software that can use computer-morphing attempting to respond to these two obstacles. It is identified as the Method of Analysis and Research of the Integration of Emotions (M.A.R.I.E.). Our primary goal is to use M.A.R.I.E. to understand the physiology of ViFaEmRe in normal healthy subjects by standardizing the measurements. Then, it will allow us to focus on subjects manifesting abnormalities in this ability. Our second goal is to make our contribution to the progress of AI hoping to add the dimension of recognition of facial emotional expressions. Objective: To study: 1) categorical vs dimensional aspects of recognition of ViFaEmRe, 2) universality vs idiosyncrasy, 3) immediate vs ambivalent Emotional-Decision-Making, 4) the Emotional-Fingerprint of a face and 5) creation of population references data. Methods: M.A.R.I.E. enables the rational, quantified measurement of Emotional Visual Acuity (EVA) in an individual observer and a population aged 20 to 70 years. Meanwhile, it can measure the range and intensity of expressed emotions through three Face- Tests, quantify the performance of a sample of 204 observers with hypernormal measures of cognition, “thymia” (defined elsewhere), and low levels of anxiety, and perform analysis of the six primary emotions. Results: We have individualized the following continuous parameters: 1) “Emotional-Visual- Acuity”, 2) “Visual-Emotional-Feeling”, 3) “Emotional-Quotient”, 4) “Emotional-Decision-Making”, 5) “Emotional-Decision-Making Graph” or “Individual-Gun-Trigger”, 6) “Emotional-Fingerprint” or “Key-graph”, 7) “Emotional-Fingerprint-Graph”, 8) detecting “misunderstanding” and 9) detecting “error”. This allowed us a taxonomy with coding of the face-emotion pair. Each face has specific measurements and graphics. The EVA improves from ages of 20 to 55 years, then decreases. It does not depend on the sex of the observer, nor the face studied. In addition, 1% of people endowed with normal intelligence do not recognize emotions. The categorical dimension is a variable for everyone. The range and intensity of ViFaEmRe is idiosyncratic and not universally uniform. The recognition of emotions is purely categorical for a single individual. It is dimensional for a population sample. Conclusions: Firstly, M.A.R.I.E. has made possible to bring out new concepts and new continuous measurements variables. The comparison between healthy and abnormal individuals makes it possible to take into consideration the significance of this line of study. From now on, these new functional parameters will allow us to identify and name “emotional” disorders or illnesses which can give additional dimension to behavioral disorders in all pathologies that affect the brain. Secondly, the ViFaEmRe is idiosyncratic, categorical, and a function of the identity of the observer and of the observed face. These findings stack up against Artificial Intelligence, which cannot have a globalist or regionalist algorithm that can be programmed into a robot, nor can AI compete with human abilities and judgment in this domain. *Here “Emotional disorders” refers to disorders of emotional expressions and recognition.展开更多
在基于Mesh-under的IPv6低功耗无线个域网(IPv6over low-power wireless personal area networks,6LoWPAN)中,针对传输路径上中间节点重传缓存溢出导致重传数据分片丢失,造成网络性能下降等问题,提出一种基于Mesh-under的备用缓存机制...在基于Mesh-under的IPv6低功耗无线个域网(IPv6over low-power wireless personal area networks,6LoWPAN)中,针对传输路径上中间节点重传缓存溢出导致重传数据分片丢失,造成网络性能下降等问题,提出一种基于Mesh-under的备用缓存机制。本文所提机制根据传输路径上各节点重传缓存使用情况及数据分片剩余跳数等信息,设置动态重传缓存门限,并为超过该门限的节点从其邻居节点中挑选合适的备用缓存节点,从而完成数据分片的缓存与重传过程,达到均衡使用各节点重传缓存的目的。结果表明,所提机制能够有效避免重传缓存溢出,减小网络能耗,同时进一步提高目的端重组成功率。展开更多
Objective: To examine and measure the decision-making processes involved in Visual Recognition of Facial Emotional Expressions (VRFEE) and to study the effects of demographic factors on this process. Method: We evalua...Objective: To examine and measure the decision-making processes involved in Visual Recognition of Facial Emotional Expressions (VRFEE) and to study the effects of demographic factors on this process. Method: We evaluated a newly designed software application (M.A.R.I.E.) that permits computerized metric measurement of VRFEE. We administered it to 204 cognitively normal participants ranging in age from 20 to 70 years. Results: We established normative values for the recognition of anger, disgust, joy, fear, surprise and sadness expressed on the faces of three individuals. There was a significant difference in the: 1) measurement (F (8.189) = 3896, p = 0.0001);2) education level (x2(12) = 28.4, p = 0.005);3) face (F(2.195) = 10, p = 0.0001);4)series (F (8.189)=28, p = 0.0001);5) interaction between the identity and recognition of emotions (F (16, 181 =11, p = 0.0001). However, performance did not differ according to: 1) age (F (6.19669) = 1.35, p = 0.2) or 2) level of education (F (1, 1587) = 0.6, p = 0.4). Conclusions: In healthy participants, the VRFEE remains stable throughout the lifespan when cognitive functions remain optimal. Disgust, sadness, fear, and joy seem to be the four most easily recognized facial emotions, while anger and surprise are not easily recognized. Visual recognition of disgust and fear is independent of aging. The characteristics of a face have a significant influence on the ease with which people recognize expressed emotions (idiosyncrasy). Perception and recognition of emotions is categorical, even when the facial images are integrated in a spectrum of morphs reflecting two different emotions on either side.展开更多
针对BOTDR分布式光纤传感技术中背向散射光中布里渊散射信号光的分离提取问题,设计了一种高消光比双通道可调M-Z干涉仪,该干涉仪由两个3 d B耦合器、电动光纤延迟线、偏振控制器及光隔离器构成。使用C波段宽带光源(ASE)对M-Z干涉仪性能...针对BOTDR分布式光纤传感技术中背向散射光中布里渊散射信号光的分离提取问题,设计了一种高消光比双通道可调M-Z干涉仪,该干涉仪由两个3 d B耦合器、电动光纤延迟线、偏振控制器及光隔离器构成。使用C波段宽带光源(ASE)对M-Z干涉仪性能进行了检测。并将脉宽为100 ns,重复频率为20 k Hz的脉冲光入射到长度为5 km的普通单模光纤中,将其产生的背向散射光经过M-Z干涉仪滤波后,通过光谱仪检测其输出的光谱信号。实验结果表明该干涉仪能够实现大范围高精度可调节滤波功能,对瑞利散射光的抑制超过20d B,可以有效地将背向散射光中的布里渊散射光信号分离提取出来。展开更多
文摘Context: The advent of Artificial Intelligence (AI) requires modeling prior to its implementation in algorithms for most human skills. This observation requires us to have a detailed and precise understanding of the interfaces of verbal and emotional communications. The progress of AI is significant on the verbal level but modest in terms of the recognition of facial emotions even if this functionality is one of the oldest in humans and is omnipresent in our daily lives. Dysfunction in the ability for facial emotional expressions is present in many brain pathologies encountered by psychiatrists, neurologists, psychotherapists, mental health professionals including social workers. It cannot be objectively verified and measured due to a lack of reliable tools that are valid and consistently sensitive. Indeed, the articles in the scientific literature dealing with Visual-Facial-Emotions-Recognition (ViFaEmRe), suffer from the absence of 1) consensual and rational tools for continuous quantified measurement, 2) operational concepts. We have invented a software that can use computer-morphing attempting to respond to these two obstacles. It is identified as the Method of Analysis and Research of the Integration of Emotions (M.A.R.I.E.). Our primary goal is to use M.A.R.I.E. to understand the physiology of ViFaEmRe in normal healthy subjects by standardizing the measurements. Then, it will allow us to focus on subjects manifesting abnormalities in this ability. Our second goal is to make our contribution to the progress of AI hoping to add the dimension of recognition of facial emotional expressions. Objective: To study: 1) categorical vs dimensional aspects of recognition of ViFaEmRe, 2) universality vs idiosyncrasy, 3) immediate vs ambivalent Emotional-Decision-Making, 4) the Emotional-Fingerprint of a face and 5) creation of population references data. Methods: With M.A.R.I.E. enable a rational quantified measurement of Emotional-Visual-Acuity (EVA) of 1) a) an individual observer, b) in a population aged 20 to 70 years old, 2) measure the range and intensity of expressed emotions by 3 Face-Tests, 3) quantify the performance of a sample of 204 observers with hyper normal measures of cognition, “thymia,” (ibid. defined elsewhere) and low levels of anxiety 4) analysis of the 6 primary emotions. Results: We have individualized the following continuous parameters: 1) “Emotional-Visual-Acuity”, 2) “Visual-Emotional-Feeling”, 3) “Emotional-Quotient”, 4) “Emotional-Deci-sion-Making”, 5) “Emotional-Decision-Making Graph” or “Individual-Gun-Trigger”6) “Emotional-Fingerprint” or “Key-graph”, 7) “Emotional-Finger-print-Graph”, 8) detecting “misunderstanding” and 9) detecting “error”. This allowed us a taxonomy with coding of the face-emotion pair. Each face has specific measurements and graphics. The EVA improves from ages of 20 to 55 years, then decreases. It does not depend on the sex of the observer, nor the face studied. In addition, 1% of people endowed with normal intelligence do not recognize emotions. The categorical dimension is a variable for everyone. The range and intensity of ViFaEmRe is idiosyncratic and not universally uniform. The recognition of emotions is purely categorical for a single individual. It is dimensional for a population sample. Conclusions: Firstly, M.A.R.I.E. has made possible to bring out new concepts and new continuous measurements variables. The comparison between healthy and abnormal individuals makes it possible to take into consideration the significance of this line of study. From now on, these new functional parameters will allow us to identify and name “emotional” disorders or illnesses which can give additional dimension to behavioral disorders in all pathologies that affect the brain. Secondly, the ViFaEmRe is idiosyncratic, categorical, and a function of the identity of the observer and of the observed face. These findings stack up against Artificial Intelligence, which cannot have a globalist or regionalist algorithm that can be programmed into a robot, nor can AI compete with human abilities and judgment in this domain. *Here “Emotional disorders” refers to disorders of emotional expressions and recognition.
文摘Context: The advent of Artificial Intelligence (AI) requires modeling prior to its implementation in algorithms for most human skills. This observation requires us to have a detailed and precise understanding of the interfaces of verbal and emotional communications. The progress of AI is significant on the verbal level but modest in terms of the recognition of facial emotions even if this functionality is one of the oldest in humans and is omnipresent in our daily lives. Dysfunction in the ability for facial emotional expressions is present in many brain pathologies encountered by psychiatrists, neurologists, psychotherapists, mental health professionals including social workers. It cannot be objectively verified and measured due to a lack of reliable tools that are valid and consistently sensitive. Indeed, the articles in the scientific literature dealing with Visual-Facial-Emotions-Recognition (ViFaEmRe), suffer from the absence of 1) consensual and rational tools for continuous quantified measurement, 2) operational concepts. We have invented a software that can use computer-morphing attempting to respond to these two obstacles. It is identified as the Method of Analysis and Research of the Integration of Emotions (M.A.R.I.E.). Our primary goal is to use M.A.R.I.E. to understand the physiology of ViFaEmRe in normal healthy subjects by standardizing the measurements. Then, it will allow us to focus on subjects manifesting abnormalities in this ability. Our second goal is to make our contribution to the progress of AI hoping to add the dimension of recognition of facial emotional expressions. Objective: To study: 1) categorical vs dimensional aspects of recognition of ViFaEmRe, 2) universality vs idiosyncrasy, 3) immediate vs ambivalent Emotional-Decision-Making, 4) the Emotional-Fingerprint of a face and 5) creation of population references data. Methods: M.A.R.I.E. enables the rational, quantified measurement of Emotional Visual Acuity (EVA) in an individual observer and a population aged 20 to 70 years. Meanwhile, it can measure the range and intensity of expressed emotions through three Face- Tests, quantify the performance of a sample of 204 observers with hypernormal measures of cognition, “thymia” (defined elsewhere), and low levels of anxiety, and perform analysis of the six primary emotions. Results: We have individualized the following continuous parameters: 1) “Emotional-Visual- Acuity”, 2) “Visual-Emotional-Feeling”, 3) “Emotional-Quotient”, 4) “Emotional-Decision-Making”, 5) “Emotional-Decision-Making Graph” or “Individual-Gun-Trigger”, 6) “Emotional-Fingerprint” or “Key-graph”, 7) “Emotional-Fingerprint-Graph”, 8) detecting “misunderstanding” and 9) detecting “error”. This allowed us a taxonomy with coding of the face-emotion pair. Each face has specific measurements and graphics. The EVA improves from ages of 20 to 55 years, then decreases. It does not depend on the sex of the observer, nor the face studied. In addition, 1% of people endowed with normal intelligence do not recognize emotions. The categorical dimension is a variable for everyone. The range and intensity of ViFaEmRe is idiosyncratic and not universally uniform. The recognition of emotions is purely categorical for a single individual. It is dimensional for a population sample. Conclusions: Firstly, M.A.R.I.E. has made possible to bring out new concepts and new continuous measurements variables. The comparison between healthy and abnormal individuals makes it possible to take into consideration the significance of this line of study. From now on, these new functional parameters will allow us to identify and name “emotional” disorders or illnesses which can give additional dimension to behavioral disorders in all pathologies that affect the brain. Secondly, the ViFaEmRe is idiosyncratic, categorical, and a function of the identity of the observer and of the observed face. These findings stack up against Artificial Intelligence, which cannot have a globalist or regionalist algorithm that can be programmed into a robot, nor can AI compete with human abilities and judgment in this domain. *Here “Emotional disorders” refers to disorders of emotional expressions and recognition.
文摘在基于Mesh-under的IPv6低功耗无线个域网(IPv6over low-power wireless personal area networks,6LoWPAN)中,针对传输路径上中间节点重传缓存溢出导致重传数据分片丢失,造成网络性能下降等问题,提出一种基于Mesh-under的备用缓存机制。本文所提机制根据传输路径上各节点重传缓存使用情况及数据分片剩余跳数等信息,设置动态重传缓存门限,并为超过该门限的节点从其邻居节点中挑选合适的备用缓存节点,从而完成数据分片的缓存与重传过程,达到均衡使用各节点重传缓存的目的。结果表明,所提机制能够有效避免重传缓存溢出,减小网络能耗,同时进一步提高目的端重组成功率。
文摘Objective: To examine and measure the decision-making processes involved in Visual Recognition of Facial Emotional Expressions (VRFEE) and to study the effects of demographic factors on this process. Method: We evaluated a newly designed software application (M.A.R.I.E.) that permits computerized metric measurement of VRFEE. We administered it to 204 cognitively normal participants ranging in age from 20 to 70 years. Results: We established normative values for the recognition of anger, disgust, joy, fear, surprise and sadness expressed on the faces of three individuals. There was a significant difference in the: 1) measurement (F (8.189) = 3896, p = 0.0001);2) education level (x2(12) = 28.4, p = 0.005);3) face (F(2.195) = 10, p = 0.0001);4)series (F (8.189)=28, p = 0.0001);5) interaction between the identity and recognition of emotions (F (16, 181 =11, p = 0.0001). However, performance did not differ according to: 1) age (F (6.19669) = 1.35, p = 0.2) or 2) level of education (F (1, 1587) = 0.6, p = 0.4). Conclusions: In healthy participants, the VRFEE remains stable throughout the lifespan when cognitive functions remain optimal. Disgust, sadness, fear, and joy seem to be the four most easily recognized facial emotions, while anger and surprise are not easily recognized. Visual recognition of disgust and fear is independent of aging. The characteristics of a face have a significant influence on the ease with which people recognize expressed emotions (idiosyncrasy). Perception and recognition of emotions is categorical, even when the facial images are integrated in a spectrum of morphs reflecting two different emotions on either side.
文摘针对BOTDR分布式光纤传感技术中背向散射光中布里渊散射信号光的分离提取问题,设计了一种高消光比双通道可调M-Z干涉仪,该干涉仪由两个3 d B耦合器、电动光纤延迟线、偏振控制器及光隔离器构成。使用C波段宽带光源(ASE)对M-Z干涉仪性能进行了检测。并将脉宽为100 ns,重复频率为20 k Hz的脉冲光入射到长度为5 km的普通单模光纤中,将其产生的背向散射光经过M-Z干涉仪滤波后,通过光谱仪检测其输出的光谱信号。实验结果表明该干涉仪能够实现大范围高精度可调节滤波功能,对瑞利散射光的抑制超过20d B,可以有效地将背向散射光中的布里渊散射光信号分离提取出来。
