Carnegie Mellon University

About: Carnegie Mellon University is a education organization based out in Pittsburgh, Pennsylvania, United States. It is known for research contribution in the topics: Computer science & Robot. The organization has 36317 authors who have published 104359 publications receiving 5975734 citations. The organization is also known as: CMU & Carnegie Mellon.

Labor Supply of New York City Cabdrivers: One Day at a Time

Abstract: Life-cycle models of labor supply predict a positive relationship between hours supplied and transitory changes in wages because such changes have virtually no effect on life-cycle wealth. Previous attempts to test this hypothesis empirically with time-series data have not been supportive; estimated elasticities are typically negative or nonsignificant. Such analyses, however, are vulnerable to measurement error and other estimation problems. We use data on daily observations of wages and hours for New York City cab drivers to estimate the supply response to transitory fluctuations in wages. Cab drivers decide daily how many hours to supply, and face wages that are positively correlated within days, but largely uncorrelated between days. Using these data, our central finding is that wage elasticities are persistently negative–from -.5 to -1 in three different samples–even after correcting for measurement error using instrumental variables. These negative wage elasticities challenge the notion that cab drivers trade off labor and leisure at different points in time and question the empirical adequacy of life-cycle formulations of labor supply.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

Finite sample properties of some alternative GMM estimators

Abstract: We investigate the small-sample properties of three alternative generalized method of moments (GMM) estimators of asset-pricing models. The estimators that we consider include ones in which the weighting matrix is iterated to convergence and ones in which the weighting matrix is changed with each choice of the parameters. Particular attention is devoted to assessing the performance of the asymptotic theory for making inferences based directly on the deterioration of GMM criterion functions.

Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter

Abstract: There is a widespread intuitive sense that different kinds of information spread differently on-line, but it has been difficult to evaluate this question quantitatively since it requires a setting where many different kinds of information spread in a shared environment. Here we study this issue on Twitter, analyzing the ways in which tokens known as hashtags spread on a network defined by the interactions among Twitter users. We find significant variation in the ways that widely-used hashtags on different topics spread.Our results show that this variation is not attributable simply to differences in "stickiness," the probability of adoption based on one or more exposures, but also to a quantity that could be viewed as a kind of "persistence" - the relative extent to which repeated exposures to a hashtag continue to have significant marginal effects. We find that hashtags on politically controversial topics are particularly persistent, with repeated exposures continuing to have unusually large marginal effects on adoption; this provides, to our knowledge, the first large-scale validation of the "complex contagion" principle from sociology, which posits that repeated exposures to an idea are particularly crucial when the idea is in some way controversial or contentious. Among other findings, we discover that hashtags representing the natural analogues of Twitter idioms and neologisms are particularly non-persistent, with the effect of multiple exposures decaying rapidly relative to the first exposure.We also study the subgraph structure of the initial adopters for different widely-adopted hashtags, again finding structural differences across topics. We develop simulation-based and generative models to analyze how the adoption dynamics interact with the network structure of the early adopters on which a hashtag spreads.

Exploiting moral wiggle room: experiments demonstrating an illusory preference for fairness

Abstract: This paper explores whether generosity in experiments is truly evidence of concern for desirable social outcomes. We conduct an experiment using a binary version of the dictator game. We introduce several treatments in which subjects are able to leave the relationship between their actions and resulting outcomes uncertain, either to themselves or to another subject influenced by those actions, thus giving subjects the moral “wiggle room” to behave self-interestedly. We find significantly less generous behavior in these manipulations, relative to a baseline in which the relationship between actions and outcomes is transparent. We conclude that many subjects behave fairly in the baseline case mainly because they intrinsically dislike appearing unfair, either to themselves or others.