Predictive modeling using structural magnetic resonance imaging (MRI) data is a prominent approach to study brain-aging. Machine learning algorithms and feature extraction methods have been employed to improve predictions and explore healthy and accelerated aging e.g. neurodegenerative and psychiatric disorders. The high-dimensional MRI data pose challenges to building generalizable and interpretable models as well as for data privacy. Common practices are resampling or averaging voxels within predefined parcels, which reduces anatomical specificity and biological interpretability as voxels within a region may differently relate to aging. Effectively, naive fusion by averaging can result in information loss and reduced accuracy. We present a conceptually novel two-level stacking ensemble (SE) approach. The first level comprises regional models for predicting individuals' age based on voxel-wise information, fused by a second-level model yielding final predictions. Eight data fusion scenarios were explored using as input Gray matter volume (GMV) estimates from four datasets covering the adult lifespan. Performance, measured using mean absolute error (MAE), R2, correlation and prediction bias, showed that SE outperformed the region-wise averages. The best performance was obtained when first-level regional predictions were obtained as out-of-sample predictions on the application site with second-level models trained on independent and site-specific data (MAE=4.75 vs baseline regional mean GMV MAE=5.68). Performance improved as more datasets were used for training. First-level predictions showed improved and more robust aging signal providing new biological insights and enhanced data privacy. Overall, the SE improves accuracy compared to the baseline while preserving or enhancing data privacy.

This paper aims to investigate and achieve seller-side fairness within online marketplaces, where many sellers and their items are not sufficiently exposed to customers in an e-commerce platform. This phenomenon raises concerns regarding the potential loss of revenue associated with less exposed items as well as less marketplace diversity. We introduce the notion of seller-side outcome fairness and build an optimization model to balance collected recommendation rewards and the fairness metric. We then propose a gradient-based data-driven algorithm based on the duality and bandit theory. Our numerical experiments on real e-commerce data sets show that our algorithm can lift seller fairness measures while not hurting metrics like collected Gross Merchandise Value (GMV) and total purchases.

GMV is looking for a highly motivated AI Engineer with specific background and interest in the application of Artificial Intelligence and Machine Learning techniques to space applications. Our new colleague will be enrolled to work on GMV's space debris and space flight dynamics activities in the frame of projects with ESA and ROSA. We work in many different sectors, like Space, Defense, Telecommunications, Security and Transportation. Do you want more information about our ambitious projects? Check our website –, you'll be surprised!

Replatforming a business of our size and scale takes time. However, our pace of innovation is accelerating. We're increasingly using structured data and artificial intelligence to transform shopping on eBay, delivering more personalization capabilities, continuing to iterate our mobile experience, and bringing more unique inventory and categories to our customers. We've got more work to do, but I'm confident we're on the right path. Now, let me turn it over to Scott, and he'll provide more details on our Q3 results.

Baird said its eBay Inc (NASDAQ: EBAY) tracker suggests steady growth trends in May, showing the month's volumes grew in the mid-single-digit range year-over-year, generally consistent with its April results. "With April and May data combined, we believe that Q2 GMV is on track for eBay, although we have yet to see a meaningful boost from recent structured data and product page improvements (more likely in 2H-2016.)," Sebastian, who maintained his Outperform rating on valuation and potential for accelerating growth, said the tracker suggests eBay's second quarter is tracking in line to potentially slightly above his 3 percent GMV growth estimate. Inc. (NASDAQ: AMZN) remained steady from April, while Google Shopping showed some acceleration to 41.5 percent from 34 percent in April. "We note that CA's data for eBay showed some modest improvement to low single-digit growth from slightly negative in April," Sebastian noted.