PAC-Bayesian Domain Adaptation Bounds for Multiclass Learners

Multiclass neural networks are a common tool in modern unsupervised domain adaptation, yet an appropriate theoretical description for their non-uniform sample complexity is lacking in the adaptation literature. To fill this gap, we propose the first PAC-Bayesian adaptation bounds for multiclass learners. We facilitate practical use of our bounds by also proposing the first approximation techniques for the multiclass distribution divergences we consider. For divergences dependent on a Gibbs predictor, we propose additional PAC-Bayesian adaptation bounds which remove the need for inefficient Monte-Carlo estimation. Empirically, we test the efficacy of our proposed approximation techniques as well as some novel design-concepts which we include in our bounds. Finally, we apply our bounds to analyze a common adaptation algorithm that uses neural networks

Understanding the Role of the Brain in Race/Ethnicity Based Stressors and Behavioral Challenges Among Youth of Color

Racial and ethnic discrimination can impact mental health, with these types of negative experiences linked to later depression, anxiety, and aggression. While these relations have now been well established in large-scale epidemiological studies, how racial and ethnic discrimination get “under the skin” to create mental health challenges is poorly understood. Suggestive data underscores that racial and ethnic discrimination may be best conceptualized as forms of chronic psychosocial stressors, especially as these experiences are linked with multiple forms of physiological dysregulation. With these changes likely impacting the brain and brain development, it will be critical to understand if racial and ethnic discrimination influence brain development during adolescence, a developmental period when the brain is rapidly changing and when mental health problems are increasing. To increase knowledge in this space, this project will leverage, the Adolescent Brain Cognitive Development (ABCD) Study (Total N=11,875) and use cutting-edge neuroimaging methods to test the hypothesis that higher levels of self-reported racial and ethnic discrimination will influence connectivity in brain circuits involved with reward and emotion-processing. Changes in these neural circuits, we hypothesize, could then create an increased risk for mental health challenges. Pinpointing critical pathways between youth of Color’s context and brain development, pathways that are typically overlooked when youth are aggregated, may be crucial for identifying targets for interventions to prevent mental health issues. Understanding these mechanisms may also give insight into brain development that may be applied to the prevention of other problem behaviors

Deep Learning-based Synthetic High-Resolution In-Depth Imaging Using an Attachable Dual-element Endoscopic Ultrasound Probe

Endoscopic ultrasound (EUS) imaging has a trade-off between resolution and penetration depth. By considering the in-vivo characteristics of human organs, it is necessary to provide clinicians with appropriate hardware specifications for precise diagnosis. Recently, super-resolution (SR) ultrasound imaging studies, including the SR task in deep learning fields, have been reported for enhancing ultrasound images. However, most of those studies did not consider ultrasound imaging natures, but rather they were conventional SR techniques based on downsampling of ultrasound images. In this study, we propose a novel deep learning-based high-resolution in-depth imaging probe capable of offering low- and high-frequency ultrasound image pairs. We developed an attachable dual-element EUS probe with customized low- and high-frequency ultrasound transducers under small hardware constraints. We also designed a special geared structure to enable the same image plane. The proposed system was evaluated with a wire phantom and a tissue-mimicking phantom. After the evaluation, 442 ultrasound image pairs from the tissue-mimicking phantom were acquired. We then applied several deep learning models to obtain synthetic high-resolution in-depth images, thus demonstrating the feasibility of our approach for clinical unmet needs. Furthermore, we quantitatively and qualitatively analyzed the results to find a suitable deep-learning model for our task. The obtained results demonstrate that our proposed dual-element EUS probe with an image-to-image translation network has the potential to provide synthetic high-frequency ultrasound images deep inside tissues.Comment: 10 pages, 9 figure