Background: Sexual deviance is regarded as an important risk factor for sexual offending. However, little is known about the development of deviant sexual interests. The transfer of arousal between emotions, i.e., excitation transfer, could attribute sexual salience to stimuli that would otherwise not be sexual in nature. As such, excitation transfer could contribute to the very beginning of unusual or deviant sexual interests. The current protocol proposes a study to investigate to what extent excitation transfer occurs, i.e., to what extent genital and subjective sexual arousal to sexual stimuli is higher in an emotional state than in a neutral state. Following a prior pilot study, several adjustments were made to the study protocol, including a stronger emotional manipulation by using 360-degree film clips and the inclusion of a larger and more sexually diverse sample. Methods: We will recruit 50 adult male volunteers with diverse sexual interests. We will induce sexual arousal in four different emotional states (aggression/dominance, endearment, fear, disgust) and a neutral state. Sexual arousal will be measured genitally using penile plethysmography and subjectively via self-report. Using paired samples t-tests, sexual arousal in the emotional states will be compared with sexual arousal in the neutral state. Discussion: We aim to show that arousal in response to emotional stimuli that are initially nonsexual in nature, can enhance sexual arousal. These findings have potentially important implications for the development of unusual and/or deviant sexual interests and possibly for the treatment of such sexual deviant interests in people who have committed sexual offenses.
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Not much is known about the etiology, or development, of deviant sexual interests. The aim of this systematic review was to provide a broad overview of current theories on the etiology of sexual deviance. We conducted a systematic search of the databases PubMed and APA PsycInfo (EBSCO). Studies were included when they discussed a theory regarding the etiology or development of sexual deviance. Included studies were assessed on quality criteria for good theories. Common etiological themes were extracted using thematic analysis. We included 47 theories explaining sexual deviance in general as well as various specific deviant sexual interests, such as pedophilia and sadism/masochism. Few theories (k = 7) were of acceptable quality as suggested by our systematic assessment of quality criteria for good theories (QUACGOT). These theories indicated that deviant sexual interests may develop as the result of an interplay of various factors: excitation transfer between emotions and sexual arousal, conditioning, problems with “normative” sexuality, and social learning. Neurobiological findings could not be included as no acceptable quality neurobiological theories could be retrieved. The important roles of excitation transfer and conditioning designate that dynamic, changeable processes take part in the etiology of sexual deviance. These same processes could potentially be deployed to diminish unwanted deviant sexual interests.
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Masonry structures represent the highest proportion of building stock worldwide. Currently, the structural condition of such structures is predominantly manually inspected which is a laborious, costly and subjective process. With developments in computer vision, there is an opportunity to use digital images to automate the visual inspection process. The aim of this study is to examine deep learning techniques for crack detection on images from masonry walls. A dataset with photos from masonry structures is produced containing complex backgrounds and various crack types and sizes. Different deep learning networks are considered and by leveraging the effect of transfer learning crack detection on masonry surfaces is performed on patch level with 95.3% accuracy and on pixel level with 79.6% F1 score. This is the first implementation of deep learning for pixel-level crack segmentation on masonry surfaces. Codes, data and networks relevant to the herein study are available in: github.com/dimitrisdais/crack_detection_CNN_masonry.
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