AS-607-03 Resolution on Endorsement of Central Coast Center for Arts Education

Endorses the creation of the Central Coast Center for Arts Education

Chapter 28- Networked Mentoring Programs: Targeted Developmental Relationships and Building a Broader Community

We introduce a targeted approach to mentoring programs that considers students’ developmental stage and fosters an inclusive mentoring community. Using the case study of Babson College’s Center for Women’s Entrepreneurial Leadership Mentoring Programs, this chapter will detail evidence-based effective practice in delivering high-quality mentoring across distinctive student populations as well as connecting students and mentor volunteers to one another to cultivate a mentoring community. We highlight three mentoring programs: the Undergraduate Near Peer, Undergraduate Professional, and Graduate mentor programs. Each program is designed to match student mentees with developmentally appropriate mentors who provide support tailored to their needs. The Undergraduate Near Peer Mentoring Program pairs first-year students with third or fourth students for adjustment to college and integration with the broader community of diverse leaders. The Undergraduate Professional Mentoring Program pairs junior and senior students with early-career professionals (3–15 years of work experience) for vocational exploration and transition to work opportunities and challenges. The Graduate Mentoring Program pairs graduate students with seasoned professionals (15+ years of executive experience) for more advanced vocational exploration and sophisticated career transition strategies for diverse leaders. Programs are designed to incorporate industry best practices, including participant input for matching, required orientation, mentorship agreements, goal setting, and resources. Across all programs, students and mentors are encouraged to connect with one another through formal program opportunities and to develop a network of relationships to support their journey at Babson College and beyond

State and Local Taxation of Financial Institutions:An Opportunity for Reform

Forces at work in both public and private sectors may soon change the way state and local political subdivisions tax financial institutions. The market for financial services is changing dramatically. Governments have expanded substantially the scope of activities in which financial depositories may engage. The competitive environment for financial activities also is changing as general business corporations enter the financial services field, an area previously considered the exclusive domain of financial institutions. Financial institutions have increasing opportunities for interstate activity, which offers both risks and challenges. These changes have occurred during a period in which the extensive framework of state and federal governmental regulation and protection of financial activity has been curtailed substantially. At the same time that financial institutions adjust to the changing market for their services, state and local governments are attempting to address increasing revenue needs. Although the budget difficulties that state and local governments face are mainly unrelated to the financial industry, the governments\u27 financial crisis is magnified by an inability to collect taxes traditionally paid by financial depositories. Moreover, a series of Supreme Court and state court decisions have restricted the ability of the states to tax the principal or interest on federal obligations held by financial depositories. Partly because of the general fiscal crisis and partly because of these court decisions, a number of states are searching for a revised basis on which to tax financial institutions. State legislatures should consider carefully the changing market forces affecting the financial industry to determine the appropriate basis for taxation.This Article examines the legal developments, both in financial industry regulation and in federal limitations on state taxation, that have helped to shape the current market for financial services.T his analysis and a review of relevant tax policy issues suggest that the states\u27 interest in taxing the financial industry on a thorough but rational basis will be served best by a state income tax on financial institutions that is based on uniform jurisdictional rules and uniform apportionment standards

A method for exploratory repeated-measures analysis applied to a breast-cancer screening study

When a model may be fitted separately to each individual statistical unit, inspection of the point estimates may help the statistician to understand between-individual variability and to identify possible relationships. However, some information will be lost in such an approach because estimation uncertainty is disregarded. We present a comparative method for exploratory repeated-measures analysis to complement the point estimates that was motivated by and is demonstrated by analysis of data from the CADET II breast-cancer screening study. The approach helped to flag up some unusual reader behavior, to assess differences in performance, and to identify potential random-effects models for further analysis.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS481 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Deep Learning Discovery of Demographic Biomarkers in Echocardiography

Deep learning has been shown to accurately assess 'hidden' phenotypes and predict biomarkers from medical imaging beyond traditional clinician interpretation of medical imaging. Given the black box nature of artificial intelligence (AI) models, caution should be exercised in applying models to healthcare as prediction tasks might be short-cut by differences in demographics across disease and patient populations. Using large echocardiography datasets from two healthcare systems, we test whether it is possible to predict age, race, and sex from cardiac ultrasound images using deep learning algorithms and assess the impact of varying confounding variables. We trained video-based convolutional neural networks to predict age, sex, and race. We found that deep learning models were able to identify age and sex, while unable to reliably predict race. Without considering confounding differences between categories, the AI model predicted sex with an AUC of 0.85 (95% CI 0.84 - 0.86), age with a mean absolute error of 9.12 years (95% CI 9.00 - 9.25), and race with AUCs ranging from 0.63 - 0.71. When predicting race, we show that tuning the proportion of a confounding variable (sex) in the training data significantly impacts model AUC (ranging from 0.57 to 0.84), while in training a sex prediction model, tuning a confounder (race) did not substantially change AUC (0.81 - 0.83). This suggests a significant proportion of the model's performance on predicting race could come from confounding features being detected by AI. Further work remains to identify the particular imaging features that associate with demographic information and to better understand the risks of demographic identification in medical AI as it pertains to potentially perpetuating bias and disparities.Comment: 2450 words, 2 figure, 3 table