Sexual victimization is prevalent in the United States and overrepresented among adolescents. Research typically assesses victimization on a continuum of severity and rarely examines patterns of victimization within an individual. Using latent class analysis, the present study investigated whether meaningful classes of sexual victimization could be found based on the tactic used and severity of sexual behavior. Personal characteristics and psycho-behavioral outcomes were explored as they related to victimization classes. Peer sexual coercion experiences were examined among 657 racially diverse high school and college students, and four classes were identified: non-victims (54%), manipulated and forced fondle/intercourse (27%), poly-victimization (9.5%), and forced fondling (9.5%). Sexual victimization classes were significantly characterized in regards to childhood sexual abuse, gender, and age. The poly-victimization class (i.e., verbal coercion, substance facilitated, and physical force resulting in completed intercourse) showed the greatest level of psycho-behavioral consequences with significantly lower self-esteem, higher psychological distress, and more sexual risk taking than all other classes. The manipulated and forced class also showed significantly lower self-esteem than non-victims. Findings provide important implications for understanding patterns of sexual victimization and related consequences to help target interventions more effectively.

We demonstrated, for the first time, a machine-learning method to assist the coexistence between quantum and classical communication channels. Software-defined networking was used to successfully enable the key generation and transmission over a city and campus network.

We model and experimentally demonstrate a novel performance learning method based on monitoring and Gaussian process. After 436km dark fiber transmission the model captures most of the test data with reasonable prediction error and enables a robust QoT predictor.

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