Deep Learning: A Tutorial

Our goal is to provide a review of deep learning methods which provide insight into structured high-dimensional data. Rather than using shallow additive architectures common to most statistical models, deep learning uses layers of semi-affine input transformations to provide a predictive rule. Applying these layers of transformations leads to a set of attributes (or, features) to which probabilistic statistical methods can be applied. Thus, the best of both worlds can be achieved: scalable prediction rules fortified with uncertainty quantification, where sparse regularization finds the features.Comment: arXiv admin note: text overlap with arXiv:1808.0861

FOOD2GATHER: What is migrants’ food all about in Europe? A media discourse analysis through the lens of controversies

This report is part of the HERANET funded project FOOD2GATHER. The project aims at understanding the question of integration/exclusion of migrants through foodscapes. An important step in this direction is to analyse the contextual framework within which food-related practices, norms and values are embedded in European societies. Food controversies that have raised and have been reported in the media since the “2015 migrants’ crisis” across Europe can reveal important aspects related to such norms and values and indicate possible tensions and compromises. This report presents and discusses relevant food controversies that occurred in the six countries participating in the study (Belgium, France, Germany, Italy, Norway, and the Netherlands). This will generate a contextual overview of the integration/exclusion of migrants through foodscapes. Controversy has been used as a tool and a scanner. Each of the six FOOD2GATHER teams provided two relevant controversies that have reached media attention in the last ten years. One of the two had to be related to halal food. The analysis of the controversies has been conducted by identifying issues they tackled, agents they involved, (public) spaces and situations in which controversies took place and what they produced. A comparative analysis of relevant variables related to migrations, such as the geopolitical position of the countries, organization of reception and food provision, has been conducted as well. The six countries included in the study have different traditions related to migration and have been exposed to the “migrants’ crisis” in different ways. These differences are reflected in the proposed controversies. However, some common traits tend to emerge and reveal power relationships within societies that are different or shared by the countries involved in the project. We show that these power relationships particularly deal with the right to food, citizens’ commitment, identity, the place of religion, animal welfare and political issues. Our study indicates that analysing controversies adds an important dimension to the study of foodscapes. Food controversies that reach the media attention are seldom something migrants have brought up themselves. The migrants’ representation in the media based on food controversies indicated that migrants are given little opportunity to negotiating values and practices, as norms about “the right” quantity and quality of food tend to reproduce the food model of the country they migrate to, also when there is a “positive” focus on ethnic business. To better understand these dynamics, we propose the concept of “food encounters” and illustrate how the type of food encounters can play a role in how foodscapes could evolve or even emerge.Consumption Research Norway (SIFO), OsloMe

Deterministic Evolutionary Trajectories Influence Primary Tumor Growth: TRACERx Renal.

The evolutionary features of clear-cell renal cell carcinoma (ccRCC) have not been systematically studied to date. We analyzed 1,206 primary tumor regions from 101 patients recruited into the multi-center prospective study, TRACERx Renal. We observe up to 30 driver events per tumor and show that subclonal diversification is associated with known prognostic parameters. By resolving the patterns of driver event ordering, co-occurrence, and mutual exclusivity at clone level, we show the deterministic nature of clonal evolution. ccRCC can be grouped into seven evolutionary subtypes, ranging from tumors characterized by early fixation of multiple mutational and copy number drivers and rapid metastases to highly branched tumors with >10 subclonal drivers and extensive parallel evolution associated with attenuated progression. We identify genetic diversity and chromosomal complexity as determinants of patient outcome. Our insights reconcile the variable clinical behavior of ccRCC and suggest evolutionary potential as a biomarker for both intervention and surveillance

Sampling from Log-Concave Distributions

We consider the problem of sampling according to a distribution with log-concave density F over a convex body K ` R n . The sampling is done using a biassed random walk and we prove polynomial upper bounds on the time to get a sample point with distribution close to F . 1 Introduction This paper is concerned with the efficient sampling of random points from R n where the underlying density F is log-concave (i.e. log F is concave). This is a natural restriction which is satisfied by many common distributions e.g. the multi-variate normal. The algorithm we use generates a sample Department of Mathematics, Carnegie Mellon University, Pittsburgh PA15213, U.S.A., Supported by NSF grant CCR-9024935 y BellCore, Morristown NJ 07962, and Carnegie Mellon University, Pittsburgh, PA 15213. z Graduate School of Business, University of Chicago, Chicago IL60637, U.S.A. path from a Markov chain whose stationary distribution is (close to) F . The algorithm falls into the class of Metropo..