2,249 research outputs found
The Role of Gender in Social Network Organization
The digital traces we leave behind when engaging with the modern world offer
an interesting lens through which we study behavioral patterns as expression of
gender. Although gender differentiation has been observed in a number of
settings, the majority of studies focus on a single data stream in isolation.
Here we use a dataset of high resolution data collected using mobile phones, as
well as detailed questionnaires, to study gender differences in a large cohort.
We consider mobility behavior and individual personality traits among a group
of more than university students. We also investigate interactions among
them expressed via person-to-person contacts, interactions on online social
networks, and telecommunication. Thus, we are able to study the differences
between male and female behavior captured through a multitude of channels for a
single cohort. We find that while the two genders are similar in a number of
aspects, there are robust deviations that include multiple facets of social
interactions, suggesting the existence of inherent behavioral differences.
Finally, we quantify how aspects of an individual's characteristics and social
behavior reveals their gender by posing it as a classification problem. We ask:
How well can we distinguish between male and female study participants based on
behavior alone? Which behavioral features are most predictive
Inferring introduction routes of invasive species using approximate Bayesian computation on microsatellite data
Determining the routes of introduction provides not only information about the history of an invasion process, but also information about the origin and construction of the genetic composition of the invading population. It remains difficult, however, to infer introduction routes from molecular data because of a lack of appropriate methods. We evaluate here the use of an approximate Bayesian computation (ABC) method for estimating the probabilities of introduction routes of invasive populations based on microsatellite data. We considered the crucial case of a single source population from which two invasive populations originated either serially from a single introduction event or from two independent introduction events. Using simulated datasets, we found that the method gave correct inferences and was robust to many erroneous beliefs. The method was also more efficient than traditional methods based on raw values of statistics such as assignment likelihood or pairwise F(ST). We illustrate some of the features of our ABC method, using real microsatellite datasets obtained for invasive populations of the western corn rootworm, Diabrotica virgifera virgifera. Most computations were performed with the DIYABC program (http://www1.montpellier.inra.fr/CBGP/diyabc/)
GSplit LBI: Taming the Procedural Bias in Neuroimaging for Disease Prediction
In voxel-based neuroimage analysis, lesion features have been the main focus
in disease prediction due to their interpretability with respect to the related
diseases. However, we observe that there exists another type of features
introduced during the preprocessing steps and we call them "\textbf{Procedural
Bias}". Besides, such bias can be leveraged to improve classification accuracy.
Nevertheless, most existing models suffer from either under-fit without
considering procedural bias or poor interpretability without differentiating
such bias from lesion ones. In this paper, a novel dual-task algorithm namely
\emph{GSplit LBI} is proposed to resolve this problem. By introducing an
augmented variable enforced to be structural sparsity with a variable splitting
term, the estimators for prediction and selecting lesion features can be
optimized separately and mutually monitored by each other following an
iterative scheme. Empirical experiments have been evaluated on the Alzheimer's
Disease Neuroimaging Initiative\thinspace(ADNI) database. The advantage of
proposed model is verified by improved stability of selected lesion features
and better classification results.Comment: Conditional Accepted by Miccai,201
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