No abstract

Generative adversarial nets (GANs) and variational auto-encoders have significantly improved our distribution modeling capabilities, showing promise for dataset augmentation, image-to-image translation and feature learning. However, to model high-dimensional distributions, sequential training and stacked architectures are common, increasing the number of tunable hyper-parameters as well as the training time. Nonetheless, the sample complexity of the distance metrics remains one of the factors affecting GAN training. We first show that the recently proposed sliced Wasserstein distance has compelling sample complexity properties when compared to the Wasserstein distance. To further improve the sliced Wasserstein distance we then analyze its 'projection complexity' and develop the max-sliced Wasserstein distance which enjoys compelling sample complexity while reducing projection complexity, albeit necessitating a max estimation. We finally illustrate that the proposed distance trains GANs on high-dimensional images up to a resolution of 256x256 easily.

In understanding the brain's response to extensive practice and development of high-level, expert skill, a key question is whether the same brain structures remain involved throughout the different stages of learning and a form of adaptation occurs, or a new functional circuit is formed with some structures dropping off and others joining. After training subjects on a set of complex motor tasks (tying knots), we utilized fMRI to observe that in subjects who learned the task well new regional activity emerged in posterior medial structures, i.e. the posterior cingulate gyrus. Activation associated with weak learning of the knots involved areas that mediate visual spatial computations. Brain activity associated with no substantive learning indicated involvement of areas dedicated to the declarative aspects learning such as the anterior cingulate and prefrontal cortex. The new activation for the pattern of strong learning has alternate interpretations involving either retrieval during episodic memory or a shift toward non-executive cognitive control of the task. While these interpretations are not resolved, the study makes clear that single time-point images of motor skill can be misleading because the brain structures that implement action can change following practice.

No abstract

This study investigates the effect of arousal on visual selection processes. Arousal is predicted to narrow the window of attention surrounding a point of focus. BOLD response to a letter discrimination task was measured under aroused (aversive noise) and non-aroused conditions (n = 8). Results revealed spatially distinct responses for trials invoking a narrow versus wide attentional focus. Under arousal a wide focus showed posterior thalamic activation similar to that associated with the narrowed attentional focus. This reflects altered stimulus filtering and supported the hypothesis. Relevant neuroanatomy involving the locus coeruleus and a triangular circuit of selective attention is discussed. The data demonstrates the intersection of arousal and visual stimulus selection systems, identifies a cognitive consequence of arousal, and provides the first fMRI evidence for brain stem autonomic arousal.

Enteric duplication cysts are rare congenital anomalies that may occur anywhere along the gastrointestinal tract, most commonly involving the small bowel. The distal ileum, jejunum, and duodenum are affected in descending order of frequency. We describe a case of biliary dilatation and duodenal intussusception caused by an enteric duplication cyst in an adult patient. To our knowledge, there are no other reported cases of this entity in an adult in the English literature. Multidetector computed tomography (MDCT) findings are emphasized, and the value of multiplanar reformation (MPR) in forming a correct preoperative differential diagnosis is discussed.

Purpose: The aim of this study was to assess racial/ethnic and socioeconomic disparities in the difference between atherosclerotic vascular disease prevalence measured by a multitask convolutional neural network (CNN) deep learning model using frontal chest radiographs (CXRs) and the prevalence reflected by administrative hierarchical condition category codes in two cohorts of patients with coronavirus disease 2019 (COVID-19). Methods: A CNN model, previously published, was trained to predict atherosclerotic disease from ambulatory frontal CXRs. The model was then validated on two cohorts of patients with COVID-19: 814 ambulatory patients from a suburban location (presenting from March 14, 2020, to October 24, 2020, the internal ambulatory cohort) and 485 hospitalized patients from an inner-city location (hospitalized from March 14, 2020, to August 12, 2020, the external hospitalized cohort). The CNN model predictions were validated against electronic health record administrative codes in both cohorts and assessed using the area under the receiver operating characteristic curve (AUC). The CXRs from the ambulatory cohort were also reviewed by two board-certified radiologists and compared with the CNN-predicted values for the same cohort to produce a receiver operating characteristic curve and the AUC. The atherosclerosis diagnosis discrepancy, D vasc , referring to the difference between the predicted value and presence or absence of the vascular disease HCC categorical code, was calculated. Linear regression was performed to determine the association of D vasc with the covariates of age, sex, race/ethnicity, language preference, and social deprivation index. Logistic regression was used to look for an association between the presence of any hierarchical condition category codes with D vasc and other covariates. Results:The CNN prediction for vascular disease from frontal CXRs in the ambulatory cohort had an AUC of 0.85 (95% confidence interval, 0.82-0.89) and in the hospitalized cohort had an AUC of 0.69 (95% confidence interval, 0.64-0.75) against the electronic health record data. In the ambulatory cohort, the consensus radiologists' reading had an AUC of 0.89 (95% confidence interval, 0.86-0.92) relative to the CNN. Multivariate linear regression of D vasc in the ambulatory cohort demonstrated significant negative associations with non-English-language preference (b ¼ À0.083, P < .05) and Black or Hispanic race/ethnicity (b ¼ À0.048, P < .05) and positive associations with age (b ¼ 0.005, P < .001) and sex (b ¼ 0.044, P < .05). For the hospitalized cohort, age was also significant (b ¼ 0.003, P < .01), as was social deprivation index (b ¼ 0.002, P < .05). The D vasc variable (odds ratio [OR], 0.34), Black or Hispanic race/ethnicity (OR, 1.58), non-English-language preference (OR, 1.74), and site (OR, 0.22) were independent predictors of having one or more hierarchical condition category codes (P < .01 for all) in the combined patient cohort.

Ayis Pyrros (2005) The brain topography associated with active reversal and suppression of an ambiguous figure, European Journal of Cognitive Psychology, 17:2, 267-288,