Activating Emotional & Analytic Engagement in Blended Learning: A Multicultural Teacher Education Example

The authors share their experience in designing a blended multicultural education course that they hoped would increase the likelihood that the teachers they were educating would take up socially just dispositions. They examined their own learning using a critical friend relationship with a colleague experienced in developing technological responses that honor relational aspects of teacher education within a framework of sociocultural theory

Characteristics of Sexual Abuse in Childhood and Adolescence Influence Sexual Risk Behavior in Adulthood

Childhood and adolescent sexual abuse has been associated with subsequent (adult) sexual risk behavior, but the effects of force and type of sexual abuse on sexual behavior outcomes have been less well-studied. The present study investigated the associations between sexual abuse characteristics and later sexual risk behavior, and explored whether gender of the child/adolescent moderated these relations. Patients attending an STD clinic completed a computerized survey that assessed history of sexual abuse as well as lifetime and current sexual behavior. Participants were considered sexually abused if they reported a sexual experience (1) before age 13 with someone 5 or more years older, (2) between the ages of 13 and 16 with someone 10 or more years older, or (3) before the age of 17 involving force or coercion. Participants who were sexually abused were further categorized based on two abuse characteristics, namely, use of penetration and force. Analyses included 1177 participants (n=534 women; n=643 men). Those who reported sexual abuse involving penetration and/or force reported more adult sexual risk behavior, including the number of lifetime partners and number of previous STD diagnoses, than those who were not sexually abused and those who were abused without force or penetration. There were no significant differences in sexual risk behavior between nonabused participants and those who reported sexual abuse without force and without penetration. Gender of the child/adolescent moderated the association between sexual abuse characteristics and adult sexual risk behavior; for men, sexual abuse with force and penetration was associated with the greatest number of episodes of sex trading, whereas for women, those who were abused with penetration, regardless of whether the abuse involved force, reported the most episodes of sex trading. These findings indicate that more severe sexual abuse is associated with riskier adult sexual behavior

Entropic gradient descent algorithms and wide flat minima

The properties of flat minima in the empirical risk landscape of neural networks have been debated for some time. Increasing evidence suggests they possess better generalization capabilities with respect to sharp ones. In this work we first discuss the relationship between alternative measures of flatness: the local entropy, which is useful for analysis and algorithm development, and the local energy, which is easier to compute and was shown empirically in extensive tests on state-of-the-art networks to be the best predictor of generalization capabilities. We show semi-analytically in simple controlled scenarios that these two measures correlate strongly with each other and with generalization. Then, we extend the analysis to the deep learning scenario by extensive numerical validations. We study two algorithms, entropy-stochastic gradient descent and replicated-stochastic gradient descent, that explicitly include the local entropy in the optimization objective. We devise a training schedule by which we consistently find flatter minima (using both flatness measures), and improve the generalization error for common architectures (e.g. ResNet, EfficientNet)

Entropic gradient descent algorithms and wide flat minima

The properties of flat minima in the empirical risk landscape of neural networks have been debated for some time. Increasing evidence suggests they possess better generalization capabilities with respect to sharp ones. In this work we first discuss the relationship between alternative measures of flatness: The local entropy, which is useful for analysis and algorithm development, and the local energy, which is easier to compute and was shown empirically in extensive tests on state-of-the-art networks to be the best predictor of generalization capabilities. We show semi-analytically in simple controlled scenarios that these two measures correlate strongly with each other and with generalization. Then, we extend the analysis to the deep learning scenario by extensive numerical validations. We study two algorithms, Entropy-SGD and Replicated-SGD, that explicitly include the local entropy in the optimization objective. We devise a training schedule by which we consistently find flatter minima (using both flatness measures), and improve the generalization error for common architectures (e.g. ResNet, EfficientNet)

Mutator genomes decay, despite sustained fitness gains, in a long-term experiment with bacteria

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