Gen-VariScan
Motivation: Advances in next-generation sequencing (NGS) methods have enabled researchers and agencies to collect a wide variety of sequencing data across multiple platforms. The motivation behind such an exercise is to analyze these datasets jointly, in order to gain insights into disease prognosis, treatment, and cure. Clustering of such datasets, can provide much needed insight into biological associations. However, the differing scale, and the heterogeneity of the mixed dataset is hurdle for such analyses. Results: The paper proposes a nonparameteric Bayesian approach called Gen-VariScan for biclustering of high-dimensional mixed data. Generalized Linear Models (GLM), and latent variable approaches are utilized to integrate mixed dataset. Sparsity inducing property of Poisson Dirichlet Process (PDP) is used to identify a lower dimensional structure of mixed covariates. We apply our method to Glioblastoma Multiforme (GBM) cancer dataset. We show that cluster detection is aposteriori consistent, as number of covariates and subject grows. As a byproduct, we derive a working value approach to perform beta regression. …

Flow Map
Flow maps in cartography are a mix of maps and flow charts, that ‘show the movement of objects from one location to another, such as the number of people in a migration, the amount of goods being traded, or the number of packets in a network’. …

Time-Conditional Generative Adversarial Network (T-CGAN)
In this paper we propose a data augmentation method for time series with irregular sampling, Time-Conditional Generative Adversarial Network (T-CGAN). Our approach is based on Conditional Generative Adversarial Networks (CGAN), where the generative step is implemented by a deconvolutional NN and the discriminative step by a convolutional NN. Both the generator and the discriminator are conditioned on the sampling timestamps, to learn the hidden relationship between data and timestamps, and consequently to generate new time series. We evaluate our model with synthetic and real-world datasets. For the synthetic data, we compare the performance of a classifier trained with T-CGAN-generated data, against the performance of the same classifier trained on the original data. Results show that classifiers trained on T-CGAN-generated data perform the same as classifiers trained on real data, even with very short time series and small training sets. For the real world datasets, we compare our method with other techniques of data augmentation for time series, such as time slicing and time warping, over a classification problem with unbalanced datasets. Results show that our method always outperforms the other approaches, both in case of regularly sampled and irregularly sampled time series. We achieve particularly good performance in case with a small training set and short, noisy, irregularly-sampled time series. …

Saliency Detection
Within our line of sight there are always things that stand out more than others. If you find yourself gazing over a city from a height for example, you may be drawn to a nearby skyscraper, a flashing light or even a red coat someone is wearing below. Saliency is the aspect of any stimulus that makes it stand out from the crowd. The reason a particular stimulus has such salience may be due to contrast i.e. a white line on a black background or as a result of emotional or cognitive factors. For example, we may hone in on something because we are actively looking for it or because it triggers something in our past or memory. Saliency is most commonly discussed in relation to the visual system but it is employed by every perceptual system such as sound and touch. If we are hungry the smell of a favourite food may be highly salient for example. The mechanisms by which humans grant certain stimuli more attentional focus than others probably holds root in our evolutionary past. Our limited cognitive resources require a way to identify the most relevant stimuli for learning and or survival. The world is full of stimuli everywhere you turn and we cannot attend to all of these at once. How does our visual system know where to focus? …