Learning the Roots of Visual Domain Shift

In this paper we focus on the spatial nature of visual domain shift, attempting to learn where domain adaptation originates in each given image of the source and target set. We borrow concepts and techniques from the CNN visualization literature, and learn domainnes maps able to localize the degree of domain specificity in images. We derive from these maps features related to different domainnes levels, and we show that by considering them as a preprocessing step for a domain adaptation algorithm, the final classification performance is strongly improved. Combined with the whole image representation, these features provide state of the art results on the Office dataset.Comment: Extended Abstrac

Variables associated with odds of finishing and finish time in a 161-km ultramarathon

We sought to determine the degree to which age, sex, calendar year, previous event experience and ambient race day temperature were associated with finishing a 100-mile (161-km) trail running race and with finish time in that race. We computed separate generalized linear mixed-effects regression models for (1) odds of finishing and (2) finish times of finishers. Every starter from 1986 to 2007 was used in computing the models for odds of finishing (8,282 starts by 3,956 individuals) and every finisher in the same period was included in the models for finish time (5,276 finishes). Factors associated with improved odds of finishing included being a first-time starter and advancing calendar year. Factors associated with reduced odds of finishing included advancing age above 38 years and warmer weather. Beyond 38 years of age, women had worse odds of finishing than men. Warmer weather had a similar effect on finish rates for men and women. Finish times were slower with advancing age, slower for women than men, and less affected by warm weather for women than for men. Calendar year was not associated with finish time after adjustment for other variables

Limb Size Discrepancy in a 29 yo Male

Please view the clinical abstract in the attached PDF fil

Scalable Bayesian Non-Negative Tensor Factorization for Massive Count Data

We present a Bayesian non-negative tensor factorization model for count-valued tensor data, and develop scalable inference algorithms (both batch and online) for dealing with massive tensors. Our generative model can handle overdispersed counts as well as infer the rank of the decomposition. Moreover, leveraging a reparameterization of the Poisson distribution as a multinomial facilitates conjugacy in the model and enables simple and efficient Gibbs sampling and variational Bayes (VB) inference updates, with a computational cost that only depends on the number of nonzeros in the tensor. The model also provides a nice interpretability for the factors; in our model, each factor corresponds to a "topic". We develop a set of online inference algorithms that allow further scaling up the model to massive tensors, for which batch inference methods may be infeasible. We apply our framework on diverse real-world applications, such as \emph{multiway} topic modeling on a scientific publications database, analyzing a political science data set, and analyzing a massive household transactions data set.Comment: ECML PKDD 201

Improving the Virtual Neurosurgery Residency Interview Experience

The residency selection process has proven a challenge in the face of the Covid-19 pandemic. In the neurosurgery match, residents are chosen based on objective metrics as well as their ability to effectively work as part of a team tasked with caring for medically complex patients faced with neurosurgical conditions. As there remain limitations on the number of externships students could participate in and the Step 1 examination is expected to be reported as either pass or fail in years to come, we will have fewer objective metrics to review in the student application. We conducted a study to best select neurosurgery resident applicants who could effectively work with our team to ultimately provide effective patientcentered care. Through a post-interview survey among applicants, we identified points of improvements for the neurosurgery residency application interview

Pediatric Artificial Lung: Improving a Large Animal Model of ESLF

Undergraduate Research Opportunity Program (UROP)http://deepblue.lib.umich.edu/bitstream/2027.42/116115/1/Pediatric_Artificial_Lung_Improving_Large_Animal_Model_ESLF.pd

Cytoreductive surgery for patients with recurrent epithelial ovarian carcinoma.

OBJECTIVE: This study aims to identify favorable preoperative characteristics and examine the impact of secondary cytoreductive surgery on survival for patients with recurrent epithelial ovarian carcinoma. METHODS: Patients who underwent cytoreductive surgery for recurrent epithelial ovarian cancer were identified in our surgical database for the period 1988-2004. Patient charts were reviewed and data collected regarding patient demographics, surgical management, preoperative evaluation, perioperative complications, and oncologic outcome. RESULTS: Eighty-five patients met eligibility criteria. Preoperative factors that correlated with improved survival were disease-free interval of greater than 12 months (por=1 cm (p CONCLUSION: When selecting patients for secondary cytoreduction, the most significant preoperative factors are disease-free interval and success of a prior cytoreductive effort. Once secondary cytoreductive surgery is attempted, the most important factor for improved survival is optimal cytoreduction. Of equal importance is counseling regarding the significant risk for bowel surgery, colostomy, and complications