20,291 research outputs found
Handling oversampling in dynamic networks using link prediction
Oversampling is a common characteristic of data representing dynamic
networks. It introduces noise into representations of dynamic networks, but
there has been little work so far to compensate for it. Oversampling can affect
the quality of many important algorithmic problems on dynamic networks,
including link prediction. Link prediction seeks to predict edges that will be
added to the network given previous snapshots. We show that not only does
oversampling affect the quality of link prediction, but that we can use link
prediction to recover from the effects of oversampling. We also introduce a
novel generative model of noise in dynamic networks that represents
oversampling. We demonstrate the results of our approach on both synthetic and
real-world data.Comment: ECML/PKDD 201
Helping Students Master Concepts in Mechanics by Graphing with Spreadsheets
An example of a curricular activity to help students master concepts in mechanics is presented. Students measure positions and times of movements using calculators, and construct graphs using spreadsheets. Students learn to connect concepts in mechanics and reinforce them following a spiral approach of increasing complexity. Comments from students about the activity are also presented
Interpretable deep learning for guided structure-property explorations in photovoltaics
The performance of an organic photovoltaic device is intricately connected to
its active layer morphology. This connection between the active layer and
device performance is very expensive to evaluate, either experimentally or
computationally. Hence, designing morphologies to achieve higher performances
is non-trivial and often intractable. To solve this, we first introduce a deep
convolutional neural network (CNN) architecture that can serve as a fast and
robust surrogate for the complex structure-property map. Several tests were
performed to gain trust in this trained model. Then, we utilize this fast
framework to perform robust microstructural design to enhance device
performance.Comment: Workshop on Machine Learning for Molecules and Materials (MLMM),
Neural Information Processing Systems (NeurIPS) 2018, Montreal, Canad
A Machine Learning Approach to Improving Occupational Income Scores
Historical studies of labor markets frequently lack data on individual
income. The occupational income score (OCCSCORE) is often used as an
alternative measure of labor market outcomes. We consider the consequences of
using OCCSCORE when researchers are interested in earnings regressions. We
estimate race and gender earnings gaps in modern decennial Censuses as well as
the 1915 Iowa State Census. Using OCCSCORE biases results towards zero and can
result in estimated gaps of the wrong sign. We use a machine learning approach
to construct a new adjusted score based on industry, occupation, and
demographics. The new income score provides estimates closer to earnings
regressions. Lastly, we consider the consequences for estimates of
intergenerational mobility elasticities
The Limits of Popularity-Based Recommendations, and the Role of Social Ties
In this paper we introduce a mathematical model that captures some of the
salient features of recommender systems that are based on popularity and that
try to exploit social ties among the users. We show that, under very general
conditions, the market always converges to a steady state, for which we are
able to give an explicit form. Thanks to this we can tell rather precisely how
much a market is altered by a recommendation system, and determine the power of
users to influence others. Our theoretical results are complemented by
experiments with real world social networks showing that social graphs prevent
large market distortions in spite of the presence of highly influential users.Comment: 10 pages, 9 figures, KDD 201
- …